CS 422 - Homework 7

Due Date: Sunday, April 10 2022 11:59:59 PM Chicago Time

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2. Practicum Problems

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2.1. Feed Forward Neural Networks

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library(keras)

Warning: package ‘keras’ was built under R version 4.1.3

library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(caret)

Loading required package: ggplot2
Loading required package: lattice
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

library(tidyverse)

Warning: package ‘tidyverse’ was built under R version 4.1.3
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages --------------------------------------------------------------------------------------------- tidyverse 1.3.1 --
√ tibble  3.1.6     √ purrr   0.3.4
√ tidyr   1.2.0     √ stringr 1.4.0
√ readr   2.1.2     √ forcats 0.5.1
Warning: package ‘readr’ was built under R version 4.1.3
Warning: package ‘forcats’ was built under R version 4.1.3
-- Conflicts ------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
x purrr::lift()   masks caret::lift()

rm(list=ls())
setwd("~/Homework 7")

df <- read.csv("activity-small.csv")

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Part 2.1(a)

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set.seed(1122)
df <- df[sample(nrow(df)), ]

indx <- sample(1:nrow(df), 0.20*nrow(df))
test.df  <- df[indx, ]
train.df <- df[-indx, ]

label.test <- test.df$label
test.df$label <- NULL
test.df <- as.data.frame(scale(test.df))
test.df$label <- label.test
rm(label.test)

label.train <- train.df$label
train.df$label <- NULL
train.df <- as.data.frame(scale(train.df))
train.df$label <- label.train
rm(label.train)
rm(indx)

x.train.df <- select(train.df, -label)
x.test.df <- select(test.df, -label)

y.train.df <- train.df$label
y.train.df.ohe <- to_categorical(y.train.df)

2022-04-10 18:20:57.155069: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-04-10 18:20:57.156042: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Loaded Tensorflow version 2.8.0

y.test.df <- test.df$label
y.test.df.ohe <- to_categorical(y.test.df)

# number <- c(0)
# loss <- c(0)
# accuracy <- c(0)
# evaluation.df <- data.frame(number, loss, accuracy)

# for (i in c(3:8)) {
#   model <- keras_model_sequential() %>%
#   layer_dense(units = i, activation="relu", input_shape=c(3)) %>%
#   layer_dense(units = 4, activation="softmax")
# 
#   model %>% compile(loss = "categorical_crossentropy", 
#           optimizer="adam", 
#           metrics=c("accuracy"))
# 
#   model %>% fit(
#     data.matrix(x.train.df), 
#     y.train.df,
#     epochs=100,
#     batch_size=1,
#     validation_split=0.20)
# 
#   c(loss, accuracy) %<-% (model %>% evaluate(as.matrix(x.test.df), y.test.df))
#   
#   evaluation.df <- (evaluation.df %>% add_row(number = i, loss = loss, accuracy = loss))
#   rm(model)
# }

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After running through iterations from 4 to 9 for the number of neurons in the hidden layer numerous times, I found 8 to be giving out the highest accuracy. I iterated over these number of neurons and then recorded the accuracies into a “evaluation table” for one of the instances when I trained the models. The table can be found as “evaluation_table.csv” file.

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model <- keras_model_sequential() %>%
  layer_dense(units = 8, activation="relu", input_shape=c(3)) %>%
  layer_dense(units = 4, activation="softmax")

2022-04-10 18:21:19.038865: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2022-04-10 18:21:19.039133: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-04-10 18:21:19.039467: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-H6EH62T
2022-04-10 18:21:19.040358: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-H6EH62T
2022-04-10 18:21:19.041538: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

model

Model: "sequential"
______________________________________________________________________________________________________________________________________
 Layer (type)                                               Output Shape                                         Param #              
======================================================================================================================================
 dense_1 (Dense)                                            (None, 8)                                            32                   
                                                                                                                                      
 dense (Dense)                                              (None, 4)                                            36                   
                                                                                                                                      
======================================================================================================================================
Total params: 68
Trainable params: 68
Non-trainable params: 0
______________________________________________________________________________________________________________________________________

model %>% compile(loss = "categorical_crossentropy",
          optimizer="adam",
          metrics=c("accuracy"))

model %>% fit(
    data.matrix(x.train.df),
    y.train.df.ohe,
    epochs=100,
    batch_size=1,
    validation_split=0.20)

Epoch 1/100

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640/640 [==============================] - 1s 1ms/step - loss: 1.2315 - accuracy: 0.4688

640/640 [==============================] - 3s 3ms/step - loss: 1.2315 - accuracy: 0.4688 - val_loss: 1.0734 - val_accuracy: 0.4812
Epoch 2/100

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640/640 [==============================] - 1s 1ms/step - loss: 1.0077 - accuracy: 0.5609

640/640 [==============================] - 1s 2ms/step - loss: 1.0077 - accuracy: 0.5609 - val_loss: 0.9543 - val_accuracy: 0.5312
Epoch 3/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.9273 - accuracy: 0.5938

640/640 [==============================] - 1s 2ms/step - loss: 0.9273 - accuracy: 0.5938 - val_loss: 0.8945 - val_accuracy: 0.5938
Epoch 4/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.8686 - accuracy: 0.6609

640/640 [==============================] - 1s 2ms/step - loss: 0.8686 - accuracy: 0.6609 - val_loss: 0.8404 - val_accuracy: 0.6500
Epoch 5/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.8084 - accuracy: 0.6906 - val_loss: 0.7945 - val_accuracy: 0.7312
Epoch 6/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.7593 - accuracy: 0.7312 - val_loss: 0.7623 - val_accuracy: 0.7437
Epoch 7/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.7235 - accuracy: 0.7297 - val_loss: 0.7368 - val_accuracy: 0.7312
Epoch 8/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.7005 - accuracy: 0.7453 - val_loss: 0.7200 - val_accuracy: 0.7375
Epoch 9/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6801 - accuracy: 0.7422 - val_loss: 0.7079 - val_accuracy: 0.7563
Epoch 10/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6652 - accuracy: 0.7469 - val_loss: 0.6940 - val_accuracy: 0.7500
Epoch 11/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6523 - accuracy: 0.7563 - val_loss: 0.6827 - val_accuracy: 0.7500
Epoch 12/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6415 - accuracy: 0.7594

640/640 [==============================] - 1s 2ms/step - loss: 0.6415 - accuracy: 0.7594 - val_loss: 0.6709 - val_accuracy: 0.7437
Epoch 13/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6316 - accuracy: 0.7594

640/640 [==============================] - 1s 2ms/step - loss: 0.6316 - accuracy: 0.7594 - val_loss: 0.6586 - val_accuracy: 0.7312
Epoch 14/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6240 - accuracy: 0.7563

640/640 [==============================] - 1s 2ms/step - loss: 0.6240 - accuracy: 0.7563 - val_loss: 0.6493 - val_accuracy: 0.7375
Epoch 15/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6154 - accuracy: 0.7500

640/640 [==============================] - 1s 2ms/step - loss: 0.6154 - accuracy: 0.7500 - val_loss: 0.6404 - val_accuracy: 0.7437
Epoch 16/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6093 - accuracy: 0.7531 - val_loss: 0.6368 - val_accuracy: 0.7625
Epoch 17/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6038 - accuracy: 0.7719 - val_loss: 0.6295 - val_accuracy: 0.7375
Epoch 18/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5997 - accuracy: 0.7625

640/640 [==============================] - 1s 2ms/step - loss: 0.5997 - accuracy: 0.7625 - val_loss: 0.6248 - val_accuracy: 0.7375
Epoch 19/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5938 - accuracy: 0.7594

640/640 [==============================] - 1s 2ms/step - loss: 0.5938 - accuracy: 0.7594 - val_loss: 0.6207 - val_accuracy: 0.7437
Epoch 20/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5906 - accuracy: 0.7578

640/640 [==============================] - 1s 2ms/step - loss: 0.5906 - accuracy: 0.7578 - val_loss: 0.6191 - val_accuracy: 0.7437
Epoch 21/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5878 - accuracy: 0.7688

640/640 [==============================] - 1s 2ms/step - loss: 0.5878 - accuracy: 0.7688 - val_loss: 0.6164 - val_accuracy: 0.7250
Epoch 22/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5843 - accuracy: 0.7609

640/640 [==============================] - 1s 2ms/step - loss: 0.5843 - accuracy: 0.7609 - val_loss: 0.6114 - val_accuracy: 0.7375
Epoch 23/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5808 - accuracy: 0.7750

640/640 [==============================] - 1s 2ms/step - loss: 0.5808 - accuracy: 0.7750 - val_loss: 0.6056 - val_accuracy: 0.7375
Epoch 24/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5783 - accuracy: 0.7641

640/640 [==============================] - 1s 2ms/step - loss: 0.5783 - accuracy: 0.7641 - val_loss: 0.6022 - val_accuracy: 0.7375
Epoch 25/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5747 - accuracy: 0.7672 - val_loss: 0.5992 - val_accuracy: 0.7437
Epoch 26/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5716 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5716 - accuracy: 0.7828 - val_loss: 0.5987 - val_accuracy: 0.7312
Epoch 27/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5698 - accuracy: 0.7547 - val_loss: 0.5944 - val_accuracy: 0.7500
Epoch 28/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5674 - accuracy: 0.7641

640/640 [==============================] - 1s 2ms/step - loss: 0.5674 - accuracy: 0.7641 - val_loss: 0.5963 - val_accuracy: 0.7437
Epoch 29/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5654 - accuracy: 0.7656

640/640 [==============================] - 1s 2ms/step - loss: 0.5654 - accuracy: 0.7656 - val_loss: 0.5933 - val_accuracy: 0.7437
Epoch 30/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5638 - accuracy: 0.7688

640/640 [==============================] - 1s 2ms/step - loss: 0.5638 - accuracy: 0.7688 - val_loss: 0.5899 - val_accuracy: 0.7500
Epoch 31/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5613 - accuracy: 0.7734

640/640 [==============================] - 1s 2ms/step - loss: 0.5613 - accuracy: 0.7734 - val_loss: 0.5908 - val_accuracy: 0.7500
Epoch 32/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5572 - accuracy: 0.7688

640/640 [==============================] - 1s 2ms/step - loss: 0.5572 - accuracy: 0.7688 - val_loss: 0.5928 - val_accuracy: 0.7625
Epoch 33/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5558 - accuracy: 0.7797 - val_loss: 0.5851 - val_accuracy: 0.7500
Epoch 34/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5541 - accuracy: 0.7656

640/640 [==============================] - 1s 2ms/step - loss: 0.5541 - accuracy: 0.7656 - val_loss: 0.5876 - val_accuracy: 0.7500
Epoch 35/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5522 - accuracy: 0.7609

640/640 [==============================] - 1s 2ms/step - loss: 0.5522 - accuracy: 0.7609 - val_loss: 0.5909 - val_accuracy: 0.7563
Epoch 36/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5504 - accuracy: 0.7703

640/640 [==============================] - 1s 2ms/step - loss: 0.5504 - accuracy: 0.7703 - val_loss: 0.5868 - val_accuracy: 0.7375
Epoch 37/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5470 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5470 - accuracy: 0.7766 - val_loss: 0.5856 - val_accuracy: 0.7375
Epoch 38/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5462 - accuracy: 0.7719

640/640 [==============================] - 1s 2ms/step - loss: 0.5462 - accuracy: 0.7719 - val_loss: 0.5853 - val_accuracy: 0.7563
Epoch 39/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5437 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5437 - accuracy: 0.7766 - val_loss: 0.5829 - val_accuracy: 0.7500
Epoch 40/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5418 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5418 - accuracy: 0.7828 - val_loss: 0.5831 - val_accuracy: 0.7500
Epoch 41/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5395 - accuracy: 0.7750

640/640 [==============================] - 1s 2ms/step - loss: 0.5395 - accuracy: 0.7750 - val_loss: 0.5841 - val_accuracy: 0.7688
Epoch 42/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5380 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5380 - accuracy: 0.7844 - val_loss: 0.5842 - val_accuracy: 0.7500
Epoch 43/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5369 - accuracy: 0.7750 - val_loss: 0.5822 - val_accuracy: 0.7563
Epoch 44/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5338 - accuracy: 0.7828 - val_loss: 0.5812 - val_accuracy: 0.7437
Epoch 45/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5327 - accuracy: 0.7766 - val_loss: 0.5799 - val_accuracy: 0.7500
Epoch 46/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5302 - accuracy: 0.7750 - val_loss: 0.5789 - val_accuracy: 0.7688
Epoch 47/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5290 - accuracy: 0.7766 - val_loss: 0.5787 - val_accuracy: 0.7563
Epoch 48/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5275 - accuracy: 0.7797

640/640 [==============================] - 1s 2ms/step - loss: 0.5275 - accuracy: 0.7797 - val_loss: 0.5778 - val_accuracy: 0.7500
Epoch 49/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5261 - accuracy: 0.7797 - val_loss: 0.5746 - val_accuracy: 0.7625
Epoch 50/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5247 - accuracy: 0.7844 - val_loss: 0.5737 - val_accuracy: 0.7625
Epoch 51/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5228 - accuracy: 0.7766 - val_loss: 0.5746 - val_accuracy: 0.7625
Epoch 52/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5221 - accuracy: 0.7859 - val_loss: 0.5712 - val_accuracy: 0.7625
Epoch 53/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5187 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5187 - accuracy: 0.7859 - val_loss: 0.5710 - val_accuracy: 0.7625
Epoch 54/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5179 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5179 - accuracy: 0.7844 - val_loss: 0.5692 - val_accuracy: 0.7625
Epoch 55/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5172 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5172 - accuracy: 0.7844 - val_loss: 0.5701 - val_accuracy: 0.7563
Epoch 56/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5154 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5154 - accuracy: 0.7844 - val_loss: 0.5705 - val_accuracy: 0.7625
Epoch 57/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5144 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5144 - accuracy: 0.7828 - val_loss: 0.5680 - val_accuracy: 0.7688
Epoch 58/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5129 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5129 - accuracy: 0.7859 - val_loss: 0.5699 - val_accuracy: 0.7625
Epoch 59/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5119 - accuracy: 0.7812

640/640 [==============================] - 1s 2ms/step - loss: 0.5119 - accuracy: 0.7812 - val_loss: 0.5693 - val_accuracy: 0.7563
Epoch 60/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5107 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5107 - accuracy: 0.7844 - val_loss: 0.5690 - val_accuracy: 0.7688
Epoch 61/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5092 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5092 - accuracy: 0.7891 - val_loss: 0.5653 - val_accuracy: 0.7563
Epoch 62/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5081 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5081 - accuracy: 0.7844 - val_loss: 0.5633 - val_accuracy: 0.7688
Epoch 63/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5078 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5078 - accuracy: 0.7828 - val_loss: 0.5630 - val_accuracy: 0.7563
Epoch 64/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5066 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.5066 - accuracy: 0.7906 - val_loss: 0.5691 - val_accuracy: 0.7500
Epoch 65/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5072 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5072 - accuracy: 0.7828 - val_loss: 0.5647 - val_accuracy: 0.7563
Epoch 66/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5047 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5047 - accuracy: 0.7859 - val_loss: 0.5594 - val_accuracy: 0.7563
Epoch 67/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5038 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5038 - accuracy: 0.7859 - val_loss: 0.5612 - val_accuracy: 0.7625
Epoch 68/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5038 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.5038 - accuracy: 0.7906 - val_loss: 0.5579 - val_accuracy: 0.7563
Epoch 69/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5024 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.5024 - accuracy: 0.7969 - val_loss: 0.5595 - val_accuracy: 0.7563
Epoch 70/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5019 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5019 - accuracy: 0.7891 - val_loss: 0.5559 - val_accuracy: 0.7500
Epoch 71/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4991 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.4991 - accuracy: 0.7922 - val_loss: 0.5612 - val_accuracy: 0.7625
Epoch 72/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4983 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4983 - accuracy: 0.7891 - val_loss: 0.5622 - val_accuracy: 0.7500
Epoch 73/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4980 - accuracy: 0.7937 - val_loss: 0.5625 - val_accuracy: 0.7500
Epoch 74/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4970 - accuracy: 0.7969 - val_loss: 0.5596 - val_accuracy: 0.7563
Epoch 75/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4971 - accuracy: 0.7844 - val_loss: 0.5608 - val_accuracy: 0.7500
Epoch 76/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4961 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4961 - accuracy: 0.7984 - val_loss: 0.5606 - val_accuracy: 0.7563
Epoch 77/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.4955 - accuracy: 0.7953 - val_loss: 0.5556 - val_accuracy: 0.7563
Epoch 78/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.4955 - accuracy: 0.7906 - val_loss: 0.5581 - val_accuracy: 0.7500
Epoch 79/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4943 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4943 - accuracy: 0.7891 - val_loss: 0.5541 - val_accuracy: 0.7563
Epoch 80/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4941 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.4941 - accuracy: 0.7922 - val_loss: 0.5563 - val_accuracy: 0.7500
Epoch 81/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4942 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.4942 - accuracy: 0.7906 - val_loss: 0.5582 - val_accuracy: 0.7500
Epoch 82/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4933 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.4933 - accuracy: 0.7906 - val_loss: 0.5571 - val_accuracy: 0.7563
Epoch 83/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4929 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4929 - accuracy: 0.8016 - val_loss: 0.5544 - val_accuracy: 0.7500
Epoch 84/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4913 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4913 - accuracy: 0.7891 - val_loss: 0.5547 - val_accuracy: 0.7563
Epoch 85/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4928 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4928 - accuracy: 0.7891 - val_loss: 0.5550 - val_accuracy: 0.7500
Epoch 86/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4918 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4918 - accuracy: 0.7891 - val_loss: 0.5557 - val_accuracy: 0.7500
Epoch 87/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4913 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4913 - accuracy: 0.7984 - val_loss: 0.5550 - val_accuracy: 0.7375
Epoch 88/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4912 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4912 - accuracy: 0.7891 - val_loss: 0.5554 - val_accuracy: 0.7500
Epoch 89/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4907 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.4907 - accuracy: 0.7922 - val_loss: 0.5548 - val_accuracy: 0.7500
Epoch 90/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4893 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.4893 - accuracy: 0.7953 - val_loss: 0.5550 - val_accuracy: 0.7500
Epoch 91/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4901 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.4901 - accuracy: 0.7937 - val_loss: 0.5531 - val_accuracy: 0.7500
Epoch 92/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4905 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4905 - accuracy: 0.8016 - val_loss: 0.5541 - val_accuracy: 0.7500
Epoch 93/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4889 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4889 - accuracy: 0.7984 - val_loss: 0.5499 - val_accuracy: 0.7500
Epoch 94/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4899 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.4899 - accuracy: 0.7859 - val_loss: 0.5571 - val_accuracy: 0.7375
Epoch 95/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4889 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4889 - accuracy: 0.8016 - val_loss: 0.5531 - val_accuracy: 0.7500
Epoch 96/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4880 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4880 - accuracy: 0.7984 - val_loss: 0.5531 - val_accuracy: 0.7437
Epoch 97/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4887 - accuracy: 0.7969 - val_loss: 0.5533 - val_accuracy: 0.7437
Epoch 98/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4881 - accuracy: 0.7922 - val_loss: 0.5501 - val_accuracy: 0.7500
Epoch 99/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4875 - accuracy: 0.7969 - val_loss: 0.5539 - val_accuracy: 0.7500
Epoch 100/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4883 - accuracy: 0.7937 - val_loss: 0.5537 - val_accuracy: 0.7437

c(loss, accuracy) %<-% (model %>% evaluate(as.matrix(x.test.df), y.test.df.ohe))

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7/7 [==============================] - 0s 2ms/step - loss: 0.7183 - accuracy: 0.7950

7/7 [==============================] - 0s 2ms/step - loss: 0.7183 - accuracy: 0.7950

pred.prob <- predict(model, as.matrix(x.test.df))

pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)

confusion.matrix <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

Part 2.1(a)(i)

paste0("Overall Accuracy: ", signif(confusion.matrix[["overall"]][["Accuracy"]], 4))

[1] "Overall Accuracy: 0.795"

---

Part 2.1(a)(ii)

---

paste0('Batch Gradient Descent')

[1] "Batch Gradient Descent"

paste0("Overall Accuracy: ", signif(confusion.matrix[["overall"]][["Accuracy"]], 2))

[1] "Overall Accuracy: 0.8"

paste0('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix[["byClass"]][1,11], 2))

[1] "   Class 0: Sensitivity = 0.98, Specificity = 0.96, Balanced Accuracy = 0.97"

paste0('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix[["byClass"]][2,11], 2))

[1] "   Class 1: Sensitivity = 0.83, Specificity = 0.86, Balanced Accuracy = 0.84"

paste0('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix[["byClass"]][3,11], 2))

[1] "   Class 2: Sensitivity = 0.88, Specificity = 0.94, Balanced Accuracy = 0.91"

paste0('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix[["byClass"]][4,11], 2))

[1] "   Class 3: Sensitivity = 0.46, Specificity = 0.97, Balanced Accuracy = 0.71"

---

Part 2.1(b)

---

batch_sizes <- c(1, 32, 64, 128, 256)

create_model <- function(bs) {
  model <- keras_model_sequential() %>%
    layer_dense(units = 8, activation="relu", input_shape=c(3)) %>%
    layer_dense(units = 4, activation="softmax")

  model %>% compile(loss = "categorical_crossentropy",
          optimizer="adam",
          metrics=c("accuracy"))
  
  model %>% fit(
    data.matrix(x.train.df),
    y.train.df.ohe,
    epochs=100,
    batch_size=bs,
    validation_split=0.20)
  
  return(model)
}

# Batch 1
model.1 <- NULL
begin <- Sys.time()
model.1 <- create_model(1)

Epoch 1/100

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640/640 [==============================] - 1s 899us/step - loss: 1.1625 - accuracy: 0.4719

640/640 [==============================] - 2s 2ms/step - loss: 1.1625 - accuracy: 0.4719 - val_loss: 1.0506 - val_accuracy: 0.6250
Epoch 2/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.9326 - accuracy: 0.6594 - val_loss: 0.9251 - val_accuracy: 0.6438
Epoch 3/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.8269 - accuracy: 0.6609 - val_loss: 0.8295 - val_accuracy: 0.6438
Epoch 4/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.7483 - accuracy: 0.6687

640/640 [==============================] - 1s 2ms/step - loss: 0.7483 - accuracy: 0.6687 - val_loss: 0.7637 - val_accuracy: 0.6375
Epoch 5/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6949 - accuracy: 0.6703

640/640 [==============================] - 1s 2ms/step - loss: 0.6949 - accuracy: 0.6703 - val_loss: 0.7131 - val_accuracy: 0.6375
Epoch 6/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6580 - accuracy: 0.6703

640/640 [==============================] - 1s 2ms/step - loss: 0.6580 - accuracy: 0.6703 - val_loss: 0.6882 - val_accuracy: 0.6313
Epoch 7/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6364 - accuracy: 0.6906

640/640 [==============================] - 1s 2ms/step - loss: 0.6364 - accuracy: 0.6906 - val_loss: 0.6701 - val_accuracy: 0.6625
Epoch 8/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6200 - accuracy: 0.7000

640/640 [==============================] - 1s 2ms/step - loss: 0.6200 - accuracy: 0.7000 - val_loss: 0.6514 - val_accuracy: 0.7063
Epoch 9/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6123 - accuracy: 0.7234

640/640 [==============================] - 1s 2ms/step - loss: 0.6123 - accuracy: 0.7234 - val_loss: 0.6449 - val_accuracy: 0.6938
Epoch 10/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6033 - accuracy: 0.7141

640/640 [==============================] - 1s 2ms/step - loss: 0.6033 - accuracy: 0.7141 - val_loss: 0.6373 - val_accuracy: 0.7000
Epoch 11/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5965 - accuracy: 0.7266

640/640 [==============================] - 1s 2ms/step - loss: 0.5965 - accuracy: 0.7266 - val_loss: 0.6369 - val_accuracy: 0.7188
Epoch 12/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5915 - accuracy: 0.7297

640/640 [==============================] - 1s 2ms/step - loss: 0.5915 - accuracy: 0.7297 - val_loss: 0.6273 - val_accuracy: 0.6750
Epoch 13/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5865 - accuracy: 0.7172

640/640 [==============================] - 1s 2ms/step - loss: 0.5865 - accuracy: 0.7172 - val_loss: 0.6227 - val_accuracy: 0.7125
Epoch 14/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5831 - accuracy: 0.7547

640/640 [==============================] - 1s 2ms/step - loss: 0.5831 - accuracy: 0.7547 - val_loss: 0.6178 - val_accuracy: 0.7312
Epoch 15/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5781 - accuracy: 0.7531

640/640 [==============================] - 1s 2ms/step - loss: 0.5781 - accuracy: 0.7531 - val_loss: 0.6135 - val_accuracy: 0.7312
Epoch 16/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5741 - accuracy: 0.7391 - val_loss: 0.6158 - val_accuracy: 0.7250
Epoch 17/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5701 - accuracy: 0.7547

640/640 [==============================] - 1s 2ms/step - loss: 0.5701 - accuracy: 0.7547 - val_loss: 0.6165 - val_accuracy: 0.7312
Epoch 18/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5669 - accuracy: 0.7641 - val_loss: 0.6061 - val_accuracy: 0.7500
Epoch 19/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5635 - accuracy: 0.7625

640/640 [==============================] - 1s 2ms/step - loss: 0.5635 - accuracy: 0.7625 - val_loss: 0.6043 - val_accuracy: 0.7437
Epoch 20/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5597 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5597 - accuracy: 0.7766 - val_loss: 0.6003 - val_accuracy: 0.7375
Epoch 21/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5549 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5549 - accuracy: 0.7766 - val_loss: 0.5982 - val_accuracy: 0.7125
Epoch 22/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5519 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5519 - accuracy: 0.7828 - val_loss: 0.5937 - val_accuracy: 0.7125
Epoch 23/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5487 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.5487 - accuracy: 0.7922 - val_loss: 0.5901 - val_accuracy: 0.7312
Epoch 24/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5458 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5458 - accuracy: 0.7891 - val_loss: 0.5867 - val_accuracy: 0.7375
Epoch 25/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5422 - accuracy: 0.7812

640/640 [==============================] - 1s 2ms/step - loss: 0.5422 - accuracy: 0.7812 - val_loss: 0.5853 - val_accuracy: 0.7500
Epoch 26/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5387 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.5387 - accuracy: 0.7937 - val_loss: 0.5874 - val_accuracy: 0.7375
Epoch 27/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5377 - accuracy: 0.7922 - val_loss: 0.5860 - val_accuracy: 0.7312
Epoch 28/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5347 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5347 - accuracy: 0.7953 - val_loss: 0.5826 - val_accuracy: 0.7312
Epoch 29/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5324 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.5324 - accuracy: 0.7937 - val_loss: 0.5792 - val_accuracy: 0.7375
Epoch 30/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5296 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5296 - accuracy: 0.7984 - val_loss: 0.5787 - val_accuracy: 0.7437
Epoch 31/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5281 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5281 - accuracy: 0.7891 - val_loss: 0.5788 - val_accuracy: 0.7563
Epoch 32/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5256 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5256 - accuracy: 0.8016 - val_loss: 0.5780 - val_accuracy: 0.7375
Epoch 33/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5255 - accuracy: 0.8000

640/640 [==============================] - 1s 2ms/step - loss: 0.5255 - accuracy: 0.8000 - val_loss: 0.5752 - val_accuracy: 0.7375
Epoch 34/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5229 - accuracy: 0.8000 - val_loss: 0.5725 - val_accuracy: 0.7250
Epoch 35/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5222 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5222 - accuracy: 0.8016 - val_loss: 0.5733 - val_accuracy: 0.7375
Epoch 36/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5200 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5200 - accuracy: 0.8016 - val_loss: 0.5761 - val_accuracy: 0.7500
Epoch 37/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5188 - accuracy: 0.7984 - val_loss: 0.5751 - val_accuracy: 0.7625
Epoch 38/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5177 - accuracy: 0.8062 - val_loss: 0.5699 - val_accuracy: 0.7312
Epoch 39/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5167 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.5167 - accuracy: 0.7969 - val_loss: 0.5700 - val_accuracy: 0.7250
Epoch 40/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5155 - accuracy: 0.8047 - val_loss: 0.5709 - val_accuracy: 0.7312
Epoch 41/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5157 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.5157 - accuracy: 0.8062 - val_loss: 0.5685 - val_accuracy: 0.7375
Epoch 42/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5139 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.5139 - accuracy: 0.8031 - val_loss: 0.5664 - val_accuracy: 0.7375
Epoch 43/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5104 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.5104 - accuracy: 0.7922 - val_loss: 0.5758 - val_accuracy: 0.7437
Epoch 44/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5131 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.5131 - accuracy: 0.8047 - val_loss: 0.5698 - val_accuracy: 0.7375
Epoch 45/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5120 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.5120 - accuracy: 0.8078 - val_loss: 0.5676 - val_accuracy: 0.7375
Epoch 46/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5105 - accuracy: 0.8062 - val_loss: 0.5647 - val_accuracy: 0.7375
Epoch 47/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5099 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5099 - accuracy: 0.8016 - val_loss: 0.5664 - val_accuracy: 0.7375
Epoch 48/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5096 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.5096 - accuracy: 0.8031 - val_loss: 0.5634 - val_accuracy: 0.7250
Epoch 49/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5096 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.5096 - accuracy: 0.8062 - val_loss: 0.5650 - val_accuracy: 0.7312
Epoch 50/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5082 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.5082 - accuracy: 0.8094 - val_loss: 0.5653 - val_accuracy: 0.7375
Epoch 51/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5072 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.5072 - accuracy: 0.8062 - val_loss: 0.5707 - val_accuracy: 0.7188
Epoch 52/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5074 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.5074 - accuracy: 0.8078 - val_loss: 0.5607 - val_accuracy: 0.7312
Epoch 53/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5071 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.5071 - accuracy: 0.8094 - val_loss: 0.5640 - val_accuracy: 0.7375
Epoch 54/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5063 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.5063 - accuracy: 0.8062 - val_loss: 0.5630 - val_accuracy: 0.7250
Epoch 55/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5055 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5055 - accuracy: 0.7984 - val_loss: 0.5656 - val_accuracy: 0.7437
Epoch 56/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5048 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.5048 - accuracy: 0.8047 - val_loss: 0.5650 - val_accuracy: 0.7312
Epoch 57/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5057 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.5057 - accuracy: 0.8062 - val_loss: 0.5620 - val_accuracy: 0.7375
Epoch 58/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5039 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5039 - accuracy: 0.8016 - val_loss: 0.5674 - val_accuracy: 0.7500
Epoch 59/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5033 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.5033 - accuracy: 0.8094 - val_loss: 0.5621 - val_accuracy: 0.7250
Epoch 60/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5038 - accuracy: 0.8062 - val_loss: 0.5632 - val_accuracy: 0.7250
Epoch 61/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5035 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.5035 - accuracy: 0.8078 - val_loss: 0.5643 - val_accuracy: 0.7437
Epoch 62/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5038 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5038 - accuracy: 0.8016 - val_loss: 0.5589 - val_accuracy: 0.7437
Epoch 63/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5032 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.5032 - accuracy: 0.8047 - val_loss: 0.5601 - val_accuracy: 0.7250
Epoch 64/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5027 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5027 - accuracy: 0.7984 - val_loss: 0.5612 - val_accuracy: 0.7250
Epoch 65/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5023 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.5023 - accuracy: 0.8094 - val_loss: 0.5591 - val_accuracy: 0.7312
Epoch 66/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5020 - accuracy: 0.8000

640/640 [==============================] - 1s 2ms/step - loss: 0.5020 - accuracy: 0.8000 - val_loss: 0.5605 - val_accuracy: 0.7312
Epoch 67/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5025 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.5025 - accuracy: 0.8047 - val_loss: 0.5595 - val_accuracy: 0.7188
Epoch 68/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5024 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5024 - accuracy: 0.7984 - val_loss: 0.5592 - val_accuracy: 0.7312
Epoch 69/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5017 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5017 - accuracy: 0.7984 - val_loss: 0.5571 - val_accuracy: 0.7250
Epoch 70/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5014 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.5014 - accuracy: 0.8078 - val_loss: 0.5585 - val_accuracy: 0.7312
Epoch 71/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5008 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.5008 - accuracy: 0.8078 - val_loss: 0.5590 - val_accuracy: 0.7125
Epoch 72/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4987 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.4987 - accuracy: 0.7937 - val_loss: 0.5664 - val_accuracy: 0.7625
Epoch 73/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4990 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.4990 - accuracy: 0.8031 - val_loss: 0.5610 - val_accuracy: 0.7437
Epoch 74/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5001 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.5001 - accuracy: 0.8047 - val_loss: 0.5607 - val_accuracy: 0.7312
Epoch 75/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4996 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4996 - accuracy: 0.8078 - val_loss: 0.5636 - val_accuracy: 0.7437
Epoch 76/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4998 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4998 - accuracy: 0.8078 - val_loss: 0.5593 - val_accuracy: 0.7375
Epoch 77/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4986 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.4986 - accuracy: 0.7969 - val_loss: 0.5625 - val_accuracy: 0.7312
Epoch 78/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4998 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4998 - accuracy: 0.8094 - val_loss: 0.5633 - val_accuracy: 0.7312
Epoch 79/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4991 - accuracy: 0.8078 - val_loss: 0.5571 - val_accuracy: 0.7375
Epoch 80/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4996 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.4996 - accuracy: 0.7969 - val_loss: 0.5592 - val_accuracy: 0.7250
Epoch 81/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4980 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4980 - accuracy: 0.8094 - val_loss: 0.5594 - val_accuracy: 0.7188
Epoch 82/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4989 - accuracy: 0.7969 - val_loss: 0.5600 - val_accuracy: 0.7250
Epoch 83/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4969 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4969 - accuracy: 0.8109 - val_loss: 0.5632 - val_accuracy: 0.7250
Epoch 84/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4991 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4991 - accuracy: 0.8062 - val_loss: 0.5589 - val_accuracy: 0.7312
Epoch 85/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4972 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.4972 - accuracy: 0.7937 - val_loss: 0.5633 - val_accuracy: 0.7563
Epoch 86/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4980 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.4980 - accuracy: 0.8031 - val_loss: 0.5619 - val_accuracy: 0.7250
Epoch 87/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4970 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4970 - accuracy: 0.8016 - val_loss: 0.5643 - val_accuracy: 0.7625
Epoch 88/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4981 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.4981 - accuracy: 0.8031 - val_loss: 0.5583 - val_accuracy: 0.7375
Epoch 89/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4960 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4960 - accuracy: 0.8016 - val_loss: 0.5620 - val_accuracy: 0.7563
Epoch 90/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4970 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4970 - accuracy: 0.8078 - val_loss: 0.5617 - val_accuracy: 0.7375
Epoch 91/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4966 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4966 - accuracy: 0.8062 - val_loss: 0.5600 - val_accuracy: 0.7375
Epoch 92/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4964 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4964 - accuracy: 0.7984 - val_loss: 0.5576 - val_accuracy: 0.7375
Epoch 93/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4960 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4960 - accuracy: 0.8094 - val_loss: 0.5609 - val_accuracy: 0.7250
Epoch 94/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4936 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.4936 - accuracy: 0.8031 - val_loss: 0.5640 - val_accuracy: 0.7688
Epoch 95/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4968 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4968 - accuracy: 0.8109 - val_loss: 0.5608 - val_accuracy: 0.7437
Epoch 96/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4951 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4951 - accuracy: 0.8172 - val_loss: 0.5658 - val_accuracy: 0.7563
Epoch 97/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.4957 - accuracy: 0.8047 - val_loss: 0.5634 - val_accuracy: 0.7312
Epoch 98/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4963 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4963 - accuracy: 0.8062 - val_loss: 0.5579 - val_accuracy: 0.7375
Epoch 99/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4957 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.4957 - accuracy: 0.8047 - val_loss: 0.5582 - val_accuracy: 0.7375
Epoch 100/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4948 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4948 - accuracy: 0.8078 - val_loss: 0.5549 - val_accuracy: 0.7312

end <- Sys.time()

runtime.1 <- round(as.numeric(difftime(time1 = end, time2 = begin, units = "secs")), 2)
  
pred.prob <- predict(model.1, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.1 <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# Batch 32
model.32 <- NULL
begin <- Sys.time()
model.32 <- create_model(32)

Epoch 1/100

 1/20 [>.............................] - ETA: 5s - loss: 1.4393 - accuracy: 0.2812
20/20 [==============================] - 0s 1ms/step - loss: 1.3494 - accuracy: 0.3016

20/20 [==============================] - 1s 36ms/step - loss: 1.3494 - accuracy: 0.3016 - val_loss: 1.3370 - val_accuracy: 0.2750
Epoch 2/100

 1/20 [>.............................] - ETA: 0s - loss: 1.2632 - accuracy: 0.2812
20/20 [==============================] - 0s 2ms/step - loss: 1.2902 - accuracy: 0.2984

20/20 [==============================] - 0s 10ms/step - loss: 1.2902 - accuracy: 0.2984 - val_loss: 1.2830 - val_accuracy: 0.3063
Epoch 3/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0732 - accuracy: 0.4688
20/20 [==============================] - 0s 1ms/step - loss: 1.2372 - accuracy: 0.3234

20/20 [==============================] - 0s 9ms/step - loss: 1.2372 - accuracy: 0.3234 - val_loss: 1.2359 - val_accuracy: 0.3250
Epoch 4/100

 1/20 [>.............................] - ETA: 0s - loss: 1.1697 - accuracy: 0.4062
20/20 [==============================] - 0s 1ms/step - loss: 1.1904 - accuracy: 0.4047

20/20 [==============================] - 0s 10ms/step - loss: 1.1904 - accuracy: 0.4047 - val_loss: 1.1951 - val_accuracy: 0.4812
Epoch 5/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0934 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 1.1497 - accuracy: 0.5391

20/20 [==============================] - 0s 9ms/step - loss: 1.1497 - accuracy: 0.5391 - val_loss: 1.1586 - val_accuracy: 0.5312
Epoch 6/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0964 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 1.1126 - accuracy: 0.5703

20/20 [==============================] - 0s 12ms/step - loss: 1.1126 - accuracy: 0.5703 - val_loss: 1.1268 - val_accuracy: 0.5312
Epoch 7/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0681 - accuracy: 0.5938
20/20 [==============================] - 0s 1ms/step - loss: 1.0814 - accuracy: 0.5781

20/20 [==============================] - 0s 10ms/step - loss: 1.0814 - accuracy: 0.5781 - val_loss: 1.0977 - val_accuracy: 0.5312
Epoch 8/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0527 - accuracy: 0.5938
20/20 [==============================] - 0s 1ms/step - loss: 1.0528 - accuracy: 0.5891

20/20 [==============================] - 0s 10ms/step - loss: 1.0528 - accuracy: 0.5891 - val_loss: 1.0730 - val_accuracy: 0.5375
Epoch 9/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9136 - accuracy: 0.6875
20/20 [==============================] - 0s 1ms/step - loss: 1.0277 - accuracy: 0.5953

20/20 [==============================] - 0s 10ms/step - loss: 1.0277 - accuracy: 0.5953 - val_loss: 1.0500 - val_accuracy: 0.5500
Epoch 10/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9350 - accuracy: 0.6250
20/20 [==============================] - 0s 2ms/step - loss: 1.0059 - accuracy: 0.6156

20/20 [==============================] - 0s 10ms/step - loss: 1.0059 - accuracy: 0.6156 - val_loss: 1.0288 - val_accuracy: 0.5312
Epoch 11/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8488 - accuracy: 0.6875
20/20 [==============================] - 0s 1ms/step - loss: 0.9855 - accuracy: 0.6234

20/20 [==============================] - 0s 9ms/step - loss: 0.9855 - accuracy: 0.6234 - val_loss: 1.0097 - val_accuracy: 0.5375
Epoch 12/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9548 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.9672 - accuracy: 0.6219

20/20 [==============================] - 0s 11ms/step - loss: 0.9672 - accuracy: 0.6219 - val_loss: 0.9922 - val_accuracy: 0.5375
Epoch 13/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0537 - accuracy: 0.5312
20/20 [==============================] - 0s 1ms/step - loss: 0.9516 - accuracy: 0.6297

20/20 [==============================] - 0s 9ms/step - loss: 0.9516 - accuracy: 0.6297 - val_loss: 0.9757 - val_accuracy: 0.5437
Epoch 14/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9481 - accuracy: 0.5938
20/20 [==============================] - 0s 2ms/step - loss: 0.9361 - accuracy: 0.6438

20/20 [==============================] - 0s 10ms/step - loss: 0.9361 - accuracy: 0.6438 - val_loss: 0.9605 - val_accuracy: 0.5688
Epoch 15/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9894 - accuracy: 0.5625
20/20 [==============================] - 0s 2ms/step - loss: 0.9222 - accuracy: 0.6516

20/20 [==============================] - 0s 10ms/step - loss: 0.9222 - accuracy: 0.6516 - val_loss: 0.9462 - val_accuracy: 0.5938
Epoch 16/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8654 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.9094 - accuracy: 0.6516

20/20 [==============================] - 0s 10ms/step - loss: 0.9094 - accuracy: 0.6516 - val_loss: 0.9331 - val_accuracy: 0.6000
Epoch 17/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8965 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.8974 - accuracy: 0.6578

20/20 [==============================] - 0s 10ms/step - loss: 0.8974 - accuracy: 0.6578 - val_loss: 0.9205 - val_accuracy: 0.6000
Epoch 18/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8473 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.8859 - accuracy: 0.6562

20/20 [==============================] - 0s 9ms/step - loss: 0.8859 - accuracy: 0.6562 - val_loss: 0.9089 - val_accuracy: 0.5938
Epoch 19/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9655 - accuracy: 0.4688
20/20 [==============================] - 0s 1ms/step - loss: 0.8752 - accuracy: 0.6500

20/20 [==============================] - 0s 9ms/step - loss: 0.8752 - accuracy: 0.6500 - val_loss: 0.8977 - val_accuracy: 0.5813
Epoch 20/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8437 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.8652 - accuracy: 0.6516

20/20 [==============================] - 0s 10ms/step - loss: 0.8652 - accuracy: 0.6516 - val_loss: 0.8875 - val_accuracy: 0.5625
Epoch 21/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9476 - accuracy: 0.5938
20/20 [==============================] - 0s 1ms/step - loss: 0.8550 - accuracy: 0.6594

20/20 [==============================] - 0s 10ms/step - loss: 0.8550 - accuracy: 0.6594 - val_loss: 0.8772 - val_accuracy: 0.5562
Epoch 22/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9192 - accuracy: 0.5938
20/20 [==============================] - 0s 1ms/step - loss: 0.8454 - accuracy: 0.6438

20/20 [==============================] - 0s 11ms/step - loss: 0.8454 - accuracy: 0.6438 - val_loss: 0.8670 - val_accuracy: 0.5562
Epoch 23/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7970 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.8360 - accuracy: 0.6406

20/20 [==============================] - 0s 9ms/step - loss: 0.8360 - accuracy: 0.6406 - val_loss: 0.8578 - val_accuracy: 0.5688
Epoch 24/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9459 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.8270 - accuracy: 0.6469

20/20 [==============================] - 0s 10ms/step - loss: 0.8270 - accuracy: 0.6469 - val_loss: 0.8484 - val_accuracy: 0.5750
Epoch 25/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7046 - accuracy: 0.6875
20/20 [==============================] - 0s 1ms/step - loss: 0.8183 - accuracy: 0.6516

20/20 [==============================] - 0s 9ms/step - loss: 0.8183 - accuracy: 0.6516 - val_loss: 0.8393 - val_accuracy: 0.5750
Epoch 26/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8420 - accuracy: 0.6875
20/20 [==============================] - 0s 2ms/step - loss: 0.8096 - accuracy: 0.6500

20/20 [==============================] - 0s 10ms/step - loss: 0.8096 - accuracy: 0.6500 - val_loss: 0.8307 - val_accuracy: 0.5875
Epoch 27/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7240 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.8013 - accuracy: 0.6531

20/20 [==============================] - 0s 10ms/step - loss: 0.8013 - accuracy: 0.6531 - val_loss: 0.8222 - val_accuracy: 0.5875
Epoch 28/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6888 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.7931 - accuracy: 0.6547

20/20 [==============================] - 0s 9ms/step - loss: 0.7931 - accuracy: 0.6547 - val_loss: 0.8134 - val_accuracy: 0.6313
Epoch 29/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7196 - accuracy: 0.7188
20/20 [==============================] - 0s 2ms/step - loss: 0.7849 - accuracy: 0.6609

20/20 [==============================] - 0s 10ms/step - loss: 0.7849 - accuracy: 0.6609 - val_loss: 0.8058 - val_accuracy: 0.6500
Epoch 30/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5448 - accuracy: 0.8125
20/20 [==============================] - 0s 1ms/step - loss: 0.7771 - accuracy: 0.6547

20/20 [==============================] - 0s 9ms/step - loss: 0.7771 - accuracy: 0.6547 - val_loss: 0.7978 - val_accuracy: 0.6750
Epoch 31/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7528 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.7695 - accuracy: 0.6609

20/20 [==============================] - 0s 10ms/step - loss: 0.7695 - accuracy: 0.6609 - val_loss: 0.7905 - val_accuracy: 0.6750
Epoch 32/100

 1/20 [>.............................] - ETA: 0s - loss: 0.9727 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.7621 - accuracy: 0.6562

20/20 [==============================] - 0s 10ms/step - loss: 0.7621 - accuracy: 0.6562 - val_loss: 0.7829 - val_accuracy: 0.6875
Epoch 33/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8643 - accuracy: 0.5312
20/20 [==============================] - 0s 1ms/step - loss: 0.7548 - accuracy: 0.6609

20/20 [==============================] - 0s 10ms/step - loss: 0.7548 - accuracy: 0.6609 - val_loss: 0.7761 - val_accuracy: 0.6812
Epoch 34/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8593 - accuracy: 0.5938
20/20 [==============================] - 0s 2ms/step - loss: 0.7479 - accuracy: 0.6562

20/20 [==============================] - 0s 10ms/step - loss: 0.7479 - accuracy: 0.6562 - val_loss: 0.7696 - val_accuracy: 0.6875
Epoch 35/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8396 - accuracy: 0.5938
20/20 [==============================] - 0s 2ms/step - loss: 0.7411 - accuracy: 0.6531

20/20 [==============================] - 0s 10ms/step - loss: 0.7411 - accuracy: 0.6531 - val_loss: 0.7633 - val_accuracy: 0.6875
Epoch 36/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0316 - accuracy: 0.5625
20/20 [==============================] - 0s 2ms/step - loss: 0.7344 - accuracy: 0.6547

20/20 [==============================] - 0s 10ms/step - loss: 0.7344 - accuracy: 0.6547 - val_loss: 0.7574 - val_accuracy: 0.6875
Epoch 37/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7296 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.7281 - accuracy: 0.6578

20/20 [==============================] - 0s 10ms/step - loss: 0.7281 - accuracy: 0.6578 - val_loss: 0.7513 - val_accuracy: 0.6875
Epoch 38/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8106 - accuracy: 0.6875
20/20 [==============================] - 0s 1ms/step - loss: 0.7219 - accuracy: 0.6562

20/20 [==============================] - 0s 9ms/step - loss: 0.7219 - accuracy: 0.6562 - val_loss: 0.7453 - val_accuracy: 0.6812
Epoch 39/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6431 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.7157 - accuracy: 0.6625

20/20 [==============================] - 0s 10ms/step - loss: 0.7157 - accuracy: 0.6625 - val_loss: 0.7398 - val_accuracy: 0.6750
Epoch 40/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7462 - accuracy: 0.5938
20/20 [==============================] - 0s 2ms/step - loss: 0.7098 - accuracy: 0.6625

20/20 [==============================] - 0s 10ms/step - loss: 0.7098 - accuracy: 0.6625 - val_loss: 0.7346 - val_accuracy: 0.6750
Epoch 41/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6636 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.7046 - accuracy: 0.6734

20/20 [==============================] - 0s 10ms/step - loss: 0.7046 - accuracy: 0.6734 - val_loss: 0.7289 - val_accuracy: 0.6812
Epoch 42/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6298 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.6988 - accuracy: 0.6797

20/20 [==============================] - 0s 11ms/step - loss: 0.6988 - accuracy: 0.6797 - val_loss: 0.7241 - val_accuracy: 0.6812
Epoch 43/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5987 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6937 - accuracy: 0.6781

20/20 [==============================] - 0s 9ms/step - loss: 0.6937 - accuracy: 0.6781 - val_loss: 0.7193 - val_accuracy: 0.6812
Epoch 44/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7706 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6886 - accuracy: 0.6734

20/20 [==============================] - 0s 10ms/step - loss: 0.6886 - accuracy: 0.6734 - val_loss: 0.7146 - val_accuracy: 0.6812
Epoch 45/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7596 - accuracy: 0.6562
20/20 [==============================] - 0s 1ms/step - loss: 0.6837 - accuracy: 0.6797

20/20 [==============================] - 0s 9ms/step - loss: 0.6837 - accuracy: 0.6797 - val_loss: 0.7104 - val_accuracy: 0.6875
Epoch 46/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8018 - accuracy: 0.5625
20/20 [==============================] - 0s 2ms/step - loss: 0.6791 - accuracy: 0.6844

20/20 [==============================] - 0s 10ms/step - loss: 0.6791 - accuracy: 0.6844 - val_loss: 0.7058 - val_accuracy: 0.6938
Epoch 47/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4931 - accuracy: 0.8125
20/20 [==============================] - 0s 1ms/step - loss: 0.6746 - accuracy: 0.6938

20/20 [==============================] - 0s 11ms/step - loss: 0.6746 - accuracy: 0.6938 - val_loss: 0.7021 - val_accuracy: 0.6875
Epoch 48/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6558 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.6705 - accuracy: 0.6953

20/20 [==============================] - 0s 10ms/step - loss: 0.6705 - accuracy: 0.6953 - val_loss: 0.6985 - val_accuracy: 0.6875
Epoch 49/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5953 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.6663 - accuracy: 0.6938

20/20 [==============================] - 0s 9ms/step - loss: 0.6663 - accuracy: 0.6938 - val_loss: 0.6947 - val_accuracy: 0.6750
Epoch 50/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6576 - accuracy: 0.6250
20/20 [==============================] - 0s 2ms/step - loss: 0.6626 - accuracy: 0.6969

20/20 [==============================] - 0s 10ms/step - loss: 0.6626 - accuracy: 0.6969 - val_loss: 0.6905 - val_accuracy: 0.6812
Epoch 51/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6158 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.6584 - accuracy: 0.6938

20/20 [==============================] - 0s 10ms/step - loss: 0.6584 - accuracy: 0.6938 - val_loss: 0.6875 - val_accuracy: 0.6938
Epoch 52/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8926 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.6548 - accuracy: 0.6938

20/20 [==============================] - 0s 9ms/step - loss: 0.6548 - accuracy: 0.6938 - val_loss: 0.6843 - val_accuracy: 0.6875
Epoch 53/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6021 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6513 - accuracy: 0.7047

20/20 [==============================] - 0s 10ms/step - loss: 0.6513 - accuracy: 0.7047 - val_loss: 0.6813 - val_accuracy: 0.6812
Epoch 54/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5491 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6480 - accuracy: 0.7078

20/20 [==============================] - 0s 10ms/step - loss: 0.6480 - accuracy: 0.7078 - val_loss: 0.6782 - val_accuracy: 0.6875
Epoch 55/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5620 - accuracy: 0.8438
20/20 [==============================] - 0s 2ms/step - loss: 0.6450 - accuracy: 0.7094

20/20 [==============================] - 0s 10ms/step - loss: 0.6450 - accuracy: 0.7094 - val_loss: 0.6751 - val_accuracy: 0.6812
Epoch 56/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4933 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.6419 - accuracy: 0.7141

20/20 [==============================] - 0s 10ms/step - loss: 0.6419 - accuracy: 0.7141 - val_loss: 0.6726 - val_accuracy: 0.6875
Epoch 57/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6327 - accuracy: 0.6875
20/20 [==============================] - 0s 2ms/step - loss: 0.6388 - accuracy: 0.7156

20/20 [==============================] - 0s 10ms/step - loss: 0.6388 - accuracy: 0.7156 - val_loss: 0.6703 - val_accuracy: 0.6875
Epoch 58/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5795 - accuracy: 0.7812
20/20 [==============================] - 0s 2ms/step - loss: 0.6362 - accuracy: 0.7156

20/20 [==============================] - 0s 10ms/step - loss: 0.6362 - accuracy: 0.7156 - val_loss: 0.6676 - val_accuracy: 0.6875
Epoch 59/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5286 - accuracy: 0.8125
20/20 [==============================] - 0s 1ms/step - loss: 0.6336 - accuracy: 0.7188

20/20 [==============================] - 0s 10ms/step - loss: 0.6336 - accuracy: 0.7188 - val_loss: 0.6662 - val_accuracy: 0.6812
Epoch 60/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6034 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.6310 - accuracy: 0.7250

20/20 [==============================] - 0s 10ms/step - loss: 0.6310 - accuracy: 0.7250 - val_loss: 0.6636 - val_accuracy: 0.6875
Epoch 61/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6795 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.6286 - accuracy: 0.7281

20/20 [==============================] - 0s 9ms/step - loss: 0.6286 - accuracy: 0.7281 - val_loss: 0.6618 - val_accuracy: 0.6812
Epoch 62/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6109 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6264 - accuracy: 0.7344

20/20 [==============================] - 0s 11ms/step - loss: 0.6264 - accuracy: 0.7344 - val_loss: 0.6597 - val_accuracy: 0.6812
Epoch 63/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6933 - accuracy: 0.6875
20/20 [==============================] - 0s 2ms/step - loss: 0.6241 - accuracy: 0.7312

20/20 [==============================] - 0s 12ms/step - loss: 0.6241 - accuracy: 0.7312 - val_loss: 0.6573 - val_accuracy: 0.6938
Epoch 64/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6531 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6217 - accuracy: 0.7328

20/20 [==============================] - 0s 9ms/step - loss: 0.6217 - accuracy: 0.7328 - val_loss: 0.6561 - val_accuracy: 0.6812
Epoch 65/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6948 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.6199 - accuracy: 0.7391

20/20 [==============================] - 0s 10ms/step - loss: 0.6199 - accuracy: 0.7391 - val_loss: 0.6543 - val_accuracy: 0.6875
Epoch 66/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7890 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.6177 - accuracy: 0.7375

20/20 [==============================] - 0s 10ms/step - loss: 0.6177 - accuracy: 0.7375 - val_loss: 0.6521 - val_accuracy: 0.6812
Epoch 67/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6143 - accuracy: 0.7500
20/20 [==============================] - 0s 1ms/step - loss: 0.6158 - accuracy: 0.7312

20/20 [==============================] - 0s 10ms/step - loss: 0.6158 - accuracy: 0.7312 - val_loss: 0.6504 - val_accuracy: 0.6812
Epoch 68/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5945 - accuracy: 0.7500
20/20 [==============================] - 0s 1ms/step - loss: 0.6138 - accuracy: 0.7422

20/20 [==============================] - 0s 11ms/step - loss: 0.6138 - accuracy: 0.7422 - val_loss: 0.6492 - val_accuracy: 0.6875
Epoch 69/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6600 - accuracy: 0.6875
20/20 [==============================] - 0s 2ms/step - loss: 0.6121 - accuracy: 0.7500

20/20 [==============================] - 0s 10ms/step - loss: 0.6121 - accuracy: 0.7500 - val_loss: 0.6480 - val_accuracy: 0.6812
Epoch 70/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5912 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.6102 - accuracy: 0.7437

20/20 [==============================] - 0s 10ms/step - loss: 0.6102 - accuracy: 0.7437 - val_loss: 0.6463 - val_accuracy: 0.6875
Epoch 71/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4506 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.6086 - accuracy: 0.7453

20/20 [==============================] - 0s 9ms/step - loss: 0.6086 - accuracy: 0.7453 - val_loss: 0.6448 - val_accuracy: 0.6875
Epoch 72/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6820 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.6071 - accuracy: 0.7531

20/20 [==============================] - 0s 10ms/step - loss: 0.6071 - accuracy: 0.7531 - val_loss: 0.6438 - val_accuracy: 0.6687
Epoch 73/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6193 - accuracy: 0.8125
20/20 [==============================] - 0s 2ms/step - loss: 0.6054 - accuracy: 0.7547

20/20 [==============================] - 0s 10ms/step - loss: 0.6054 - accuracy: 0.7547 - val_loss: 0.6414 - val_accuracy: 0.6875
Epoch 74/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6891 - accuracy: 0.6562
20/20 [==============================] - 0s 1ms/step - loss: 0.6039 - accuracy: 0.7500

20/20 [==============================] - 0s 10ms/step - loss: 0.6039 - accuracy: 0.7500 - val_loss: 0.6411 - val_accuracy: 0.6875
Epoch 75/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7763 - accuracy: 0.8125
20/20 [==============================] - 0s 1ms/step - loss: 0.6025 - accuracy: 0.7531

20/20 [==============================] - 0s 10ms/step - loss: 0.6025 - accuracy: 0.7531 - val_loss: 0.6397 - val_accuracy: 0.6875
Epoch 76/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4797 - accuracy: 0.8750
20/20 [==============================] - 0s 2ms/step - loss: 0.6011 - accuracy: 0.7547

20/20 [==============================] - 0s 10ms/step - loss: 0.6011 - accuracy: 0.7547 - val_loss: 0.6384 - val_accuracy: 0.6875
Epoch 77/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6169 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.5995 - accuracy: 0.7563

20/20 [==============================] - 0s 11ms/step - loss: 0.5995 - accuracy: 0.7563 - val_loss: 0.6367 - val_accuracy: 0.6938
Epoch 78/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5320 - accuracy: 0.8125
17/20 [========================>.....] - ETA: 0s - loss: 0.5738 - accuracy: 0.7629
20/20 [==============================] - 0s 3ms/step - loss: 0.5984 - accuracy: 0.7516

20/20 [==============================] - 0s 11ms/step - loss: 0.5984 - accuracy: 0.7516 - val_loss: 0.6357 - val_accuracy: 0.7000
Epoch 79/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6620 - accuracy: 0.6562
20/20 [==============================] - 0s 1ms/step - loss: 0.5970 - accuracy: 0.7609

20/20 [==============================] - 0s 10ms/step - loss: 0.5970 - accuracy: 0.7609 - val_loss: 0.6343 - val_accuracy: 0.6812
Epoch 80/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4647 - accuracy: 0.8750
20/20 [==============================] - 0s 1ms/step - loss: 0.5956 - accuracy: 0.7594

20/20 [==============================] - 0s 9ms/step - loss: 0.5956 - accuracy: 0.7594 - val_loss: 0.6329 - val_accuracy: 0.6875
Epoch 81/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5388 - accuracy: 0.5938
20/20 [==============================] - 0s 2ms/step - loss: 0.5945 - accuracy: 0.7609

20/20 [==============================] - 0s 10ms/step - loss: 0.5945 - accuracy: 0.7609 - val_loss: 0.6318 - val_accuracy: 0.6938
Epoch 82/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5205 - accuracy: 0.7500
20/20 [==============================] - 0s 1ms/step - loss: 0.5933 - accuracy: 0.7625

20/20 [==============================] - 0s 10ms/step - loss: 0.5933 - accuracy: 0.7625 - val_loss: 0.6310 - val_accuracy: 0.7000
Epoch 83/100

 1/20 [>.............................] - ETA: 0s - loss: 0.7774 - accuracy: 0.6562
20/20 [==============================] - 0s 1ms/step - loss: 0.5920 - accuracy: 0.7641

20/20 [==============================] - 0s 9ms/step - loss: 0.5920 - accuracy: 0.7641 - val_loss: 0.6297 - val_accuracy: 0.6938
Epoch 84/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5137 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.5907 - accuracy: 0.7641

20/20 [==============================] - 0s 11ms/step - loss: 0.5907 - accuracy: 0.7641 - val_loss: 0.6289 - val_accuracy: 0.6938
Epoch 85/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6174 - accuracy: 0.7500
20/20 [==============================] - 0s 1ms/step - loss: 0.5897 - accuracy: 0.7641

20/20 [==============================] - 0s 10ms/step - loss: 0.5897 - accuracy: 0.7641 - val_loss: 0.6280 - val_accuracy: 0.7000
Epoch 86/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6110 - accuracy: 0.8438
20/20 [==============================] - 0s 1ms/step - loss: 0.5886 - accuracy: 0.7625

20/20 [==============================] - 0s 10ms/step - loss: 0.5886 - accuracy: 0.7625 - val_loss: 0.6268 - val_accuracy: 0.7063
Epoch 87/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4505 - accuracy: 0.8750
20/20 [==============================] - 0s 2ms/step - loss: 0.5874 - accuracy: 0.7641

20/20 [==============================] - 0s 10ms/step - loss: 0.5874 - accuracy: 0.7641 - val_loss: 0.6257 - val_accuracy: 0.7125
Epoch 88/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5188 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.5865 - accuracy: 0.7641

20/20 [==============================] - 0s 10ms/step - loss: 0.5865 - accuracy: 0.7641 - val_loss: 0.6244 - val_accuracy: 0.7125
Epoch 89/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5805 - accuracy: 0.7500
20/20 [==============================] - 0s 2ms/step - loss: 0.5853 - accuracy: 0.7641

20/20 [==============================] - 0s 10ms/step - loss: 0.5853 - accuracy: 0.7641 - val_loss: 0.6243 - val_accuracy: 0.7063
Epoch 90/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5555 - accuracy: 0.8438
20/20 [==============================] - 0s 1ms/step - loss: 0.5846 - accuracy: 0.7609

20/20 [==============================] - 0s 10ms/step - loss: 0.5846 - accuracy: 0.7609 - val_loss: 0.6230 - val_accuracy: 0.7125
Epoch 91/100

 1/20 [>.............................] - ETA: 0s - loss: 0.6213 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.5836 - accuracy: 0.7656

20/20 [==============================] - 0s 10ms/step - loss: 0.5836 - accuracy: 0.7656 - val_loss: 0.6217 - val_accuracy: 0.7125
Epoch 92/100

 1/20 [>.............................] - ETA: 0s - loss: 0.3318 - accuracy: 0.9375
20/20 [==============================] - 0s 1ms/step - loss: 0.5825 - accuracy: 0.7672

20/20 [==============================] - 0s 9ms/step - loss: 0.5825 - accuracy: 0.7672 - val_loss: 0.6224 - val_accuracy: 0.7063
Epoch 93/100

 1/20 [>.............................] - ETA: 0s - loss: 0.4405 - accuracy: 0.8438
20/20 [==============================] - 0s 2ms/step - loss: 0.5812 - accuracy: 0.7641

20/20 [==============================] - 0s 23ms/step - loss: 0.5812 - accuracy: 0.7641 - val_loss: 0.6209 - val_accuracy: 0.7000
Epoch 94/100

 1/20 [>.............................] - ETA: 0s - loss: 1.0776 - accuracy: 0.6250
20/20 [==============================] - 0s 1ms/step - loss: 0.5805 - accuracy: 0.7625

20/20 [==============================] - 0s 9ms/step - loss: 0.5805 - accuracy: 0.7625 - val_loss: 0.6199 - val_accuracy: 0.7000
Epoch 95/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5409 - accuracy: 0.7188
20/20 [==============================] - 0s 1ms/step - loss: 0.5796 - accuracy: 0.7672

20/20 [==============================] - 0s 9ms/step - loss: 0.5796 - accuracy: 0.7672 - val_loss: 0.6190 - val_accuracy: 0.7000
Epoch 96/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5124 - accuracy: 0.7500
20/20 [==============================] - 0s 1ms/step - loss: 0.5783 - accuracy: 0.7656

20/20 [==============================] - 0s 9ms/step - loss: 0.5783 - accuracy: 0.7656 - val_loss: 0.6183 - val_accuracy: 0.7063
Epoch 97/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5478 - accuracy: 0.8125
20/20 [==============================] - 0s 2ms/step - loss: 0.5775 - accuracy: 0.7656

20/20 [==============================] - 0s 10ms/step - loss: 0.5775 - accuracy: 0.7656 - val_loss: 0.6172 - val_accuracy: 0.7000
Epoch 98/100

 1/20 [>.............................] - ETA: 0s - loss: 0.8382 - accuracy: 0.6875
20/20 [==============================] - 0s 2ms/step - loss: 0.5765 - accuracy: 0.7688

20/20 [==============================] - 0s 10ms/step - loss: 0.5765 - accuracy: 0.7688 - val_loss: 0.6164 - val_accuracy: 0.7000
Epoch 99/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5331 - accuracy: 0.6562
20/20 [==============================] - 0s 2ms/step - loss: 0.5758 - accuracy: 0.7688

20/20 [==============================] - 0s 10ms/step - loss: 0.5758 - accuracy: 0.7688 - val_loss: 0.6166 - val_accuracy: 0.7063
Epoch 100/100

 1/20 [>.............................] - ETA: 0s - loss: 0.5726 - accuracy: 0.7812
20/20 [==============================] - 0s 1ms/step - loss: 0.5753 - accuracy: 0.7703

20/20 [==============================] - 0s 10ms/step - loss: 0.5753 - accuracy: 0.7703 - val_loss: 0.6152 - val_accuracy: 0.7000

end <- Sys.time()

runtime.32 <- round(as.numeric(difftime(time1 = end, time2 = begin, units = "secs")), 2)
  
pred.prob <- predict(model.32, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.32 <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# Batch 64
model.64 <- NULL
begin <- Sys.time()
model.64 <- create_model(64)

Epoch 1/100

 1/10 [==>...........................] - ETA: 2s - loss: 1.7413 - accuracy: 0.0312
10/10 [==============================] - 0s 1ms/step - loss: 1.7238 - accuracy: 0.0344

10/10 [==============================] - 1s 74ms/step - loss: 1.7238 - accuracy: 0.0344 - val_loss: 1.6610 - val_accuracy: 0.0437
Epoch 2/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.7070 - accuracy: 0.0156
10/10 [==============================] - 0s 2ms/step - loss: 1.6838 - accuracy: 0.0344

10/10 [==============================] - 0s 18ms/step - loss: 1.6838 - accuracy: 0.0344 - val_loss: 1.6245 - val_accuracy: 0.0437
Epoch 3/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.6372 - accuracy: 0.0000e+00
10/10 [==============================] - 0s 1ms/step - loss: 1.6456 - accuracy: 0.0328

10/10 [==============================] - 0s 19ms/step - loss: 1.6456 - accuracy: 0.0328 - val_loss: 1.5903 - val_accuracy: 0.0437
Epoch 4/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.6937 - accuracy: 0.0156
10/10 [==============================] - 0s 1ms/step - loss: 1.6098 - accuracy: 0.0344

10/10 [==============================] - 0s 19ms/step - loss: 1.6098 - accuracy: 0.0344 - val_loss: 1.5581 - val_accuracy: 0.0437
Epoch 5/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.6080 - accuracy: 0.0156
10/10 [==============================] - 0s 1ms/step - loss: 1.5758 - accuracy: 0.0375

10/10 [==============================] - 0s 17ms/step - loss: 1.5758 - accuracy: 0.0375 - val_loss: 1.5279 - val_accuracy: 0.0500
Epoch 6/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.5828 - accuracy: 0.0469
10/10 [==============================] - 0s 1ms/step - loss: 1.5444 - accuracy: 0.0406

10/10 [==============================] - 0s 19ms/step - loss: 1.5444 - accuracy: 0.0406 - val_loss: 1.4987 - val_accuracy: 0.0625
Epoch 7/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.4722 - accuracy: 0.0625
10/10 [==============================] - 0s 1ms/step - loss: 1.5132 - accuracy: 0.0406

10/10 [==============================] - 0s 19ms/step - loss: 1.5132 - accuracy: 0.0406 - val_loss: 1.4716 - val_accuracy: 0.0437
Epoch 8/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.5206 - accuracy: 0.0312
10/10 [==============================] - 0s 1ms/step - loss: 1.4844 - accuracy: 0.0500

10/10 [==============================] - 0s 21ms/step - loss: 1.4844 - accuracy: 0.0500 - val_loss: 1.4456 - val_accuracy: 0.0688
Epoch 9/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.4463 - accuracy: 0.0625
10/10 [==============================] - 0s 1ms/step - loss: 1.4567 - accuracy: 0.1016

10/10 [==============================] - 0s 19ms/step - loss: 1.4567 - accuracy: 0.1016 - val_loss: 1.4207 - val_accuracy: 0.1437
Epoch 10/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.5031 - accuracy: 0.1406
10/10 [==============================] - 0s 1ms/step - loss: 1.4306 - accuracy: 0.1734

10/10 [==============================] - 0s 19ms/step - loss: 1.4306 - accuracy: 0.1734 - val_loss: 1.3968 - val_accuracy: 0.1813
Epoch 11/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.4277 - accuracy: 0.2500
10/10 [==============================] - 0s 1ms/step - loss: 1.4052 - accuracy: 0.2297

10/10 [==============================] - 0s 22ms/step - loss: 1.4052 - accuracy: 0.2297 - val_loss: 1.3739 - val_accuracy: 0.2438
Epoch 12/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.3886 - accuracy: 0.3125
10/10 [==============================] - 0s 1ms/step - loss: 1.3810 - accuracy: 0.2734

10/10 [==============================] - 0s 20ms/step - loss: 1.3810 - accuracy: 0.2734 - val_loss: 1.3520 - val_accuracy: 0.2500
Epoch 13/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.3398 - accuracy: 0.2969
10/10 [==============================] - 0s 1ms/step - loss: 1.3576 - accuracy: 0.2984

10/10 [==============================] - 0s 19ms/step - loss: 1.3576 - accuracy: 0.2984 - val_loss: 1.3309 - val_accuracy: 0.2625
Epoch 14/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.3527 - accuracy: 0.3594
10/10 [==============================] - 0s 1ms/step - loss: 1.3351 - accuracy: 0.3078

10/10 [==============================] - 0s 20ms/step - loss: 1.3351 - accuracy: 0.3078 - val_loss: 1.3106 - val_accuracy: 0.2812
Epoch 15/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.3550 - accuracy: 0.3438
10/10 [==============================] - 0s 2ms/step - loss: 1.3133 - accuracy: 0.3203

10/10 [==============================] - 0s 21ms/step - loss: 1.3133 - accuracy: 0.3203 - val_loss: 1.2913 - val_accuracy: 0.3000
Epoch 16/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2640 - accuracy: 0.3281
10/10 [==============================] - 0s 3ms/step - loss: 1.2926 - accuracy: 0.3531

10/10 [==============================] - 0s 22ms/step - loss: 1.2926 - accuracy: 0.3531 - val_loss: 1.2725 - val_accuracy: 0.3375
Epoch 17/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2521 - accuracy: 0.4219
10/10 [==============================] - 0s 1ms/step - loss: 1.2725 - accuracy: 0.3828

10/10 [==============================] - 0s 19ms/step - loss: 1.2725 - accuracy: 0.3828 - val_loss: 1.2545 - val_accuracy: 0.3688
Epoch 18/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.3107 - accuracy: 0.2656
10/10 [==============================] - 0s 2ms/step - loss: 1.2529 - accuracy: 0.4062

10/10 [==============================] - 0s 21ms/step - loss: 1.2529 - accuracy: 0.4062 - val_loss: 1.2373 - val_accuracy: 0.3938
Epoch 19/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2594 - accuracy: 0.3750
10/10 [==============================] - 0s 2ms/step - loss: 1.2345 - accuracy: 0.4250

10/10 [==============================] - 0s 21ms/step - loss: 1.2345 - accuracy: 0.4250 - val_loss: 1.2206 - val_accuracy: 0.4000
Epoch 20/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2814 - accuracy: 0.4531
10/10 [==============================] - 0s 1ms/step - loss: 1.2160 - accuracy: 0.4469

10/10 [==============================] - 0s 19ms/step - loss: 1.2160 - accuracy: 0.4469 - val_loss: 1.2050 - val_accuracy: 0.4062
Epoch 21/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2760 - accuracy: 0.3906
10/10 [==============================] - 0s 1ms/step - loss: 1.1988 - accuracy: 0.4656

10/10 [==============================] - 0s 19ms/step - loss: 1.1988 - accuracy: 0.4656 - val_loss: 1.1899 - val_accuracy: 0.4187
Epoch 22/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2434 - accuracy: 0.4688
10/10 [==============================] - 0s 1ms/step - loss: 1.1820 - accuracy: 0.4828

10/10 [==============================] - 0s 20ms/step - loss: 1.1820 - accuracy: 0.4828 - val_loss: 1.1754 - val_accuracy: 0.4375
Epoch 23/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.1512 - accuracy: 0.4844
10/10 [==============================] - 0s 1ms/step - loss: 1.1661 - accuracy: 0.4906

10/10 [==============================] - 0s 19ms/step - loss: 1.1661 - accuracy: 0.4906 - val_loss: 1.1615 - val_accuracy: 0.4375
Epoch 24/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.1283 - accuracy: 0.4844
10/10 [==============================] - 0s 1ms/step - loss: 1.1510 - accuracy: 0.4984

10/10 [==============================] - 0s 19ms/step - loss: 1.1510 - accuracy: 0.4984 - val_loss: 1.1482 - val_accuracy: 0.4313
Epoch 25/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2069 - accuracy: 0.4844
10/10 [==============================] - 0s 2ms/step - loss: 1.1364 - accuracy: 0.5063

10/10 [==============================] - 0s 19ms/step - loss: 1.1364 - accuracy: 0.5063 - val_loss: 1.1355 - val_accuracy: 0.4437
Epoch 26/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0547 - accuracy: 0.5781
10/10 [==============================] - 0s 1ms/step - loss: 1.1224 - accuracy: 0.5125

10/10 [==============================] - 0s 21ms/step - loss: 1.1224 - accuracy: 0.5125 - val_loss: 1.1233 - val_accuracy: 0.4500
Epoch 27/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.1398 - accuracy: 0.5469
10/10 [==============================] - 0s 2ms/step - loss: 1.1092 - accuracy: 0.5172

10/10 [==============================] - 0s 19ms/step - loss: 1.1092 - accuracy: 0.5172 - val_loss: 1.1116 - val_accuracy: 0.4563
Epoch 28/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0612 - accuracy: 0.4844
10/10 [==============================] - 0s 1ms/step - loss: 1.0964 - accuracy: 0.5156

10/10 [==============================] - 0s 20ms/step - loss: 1.0964 - accuracy: 0.5156 - val_loss: 1.1004 - val_accuracy: 0.4625
Epoch 29/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0620 - accuracy: 0.5625
10/10 [==============================] - 0s 1ms/step - loss: 1.0839 - accuracy: 0.5188

10/10 [==============================] - 0s 19ms/step - loss: 1.0839 - accuracy: 0.5188 - val_loss: 1.0897 - val_accuracy: 0.4688
Epoch 30/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0818 - accuracy: 0.4844
10/10 [==============================] - 0s 1ms/step - loss: 1.0723 - accuracy: 0.5250

10/10 [==============================] - 0s 21ms/step - loss: 1.0723 - accuracy: 0.5250 - val_loss: 1.0793 - val_accuracy: 0.4750
Epoch 31/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9602 - accuracy: 0.6250
10/10 [==============================] - 0s 1ms/step - loss: 1.0611 - accuracy: 0.5312

10/10 [==============================] - 0s 21ms/step - loss: 1.0611 - accuracy: 0.5312 - val_loss: 1.0693 - val_accuracy: 0.4750
Epoch 32/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.2360 - accuracy: 0.3906
10/10 [==============================] - 0s 1ms/step - loss: 1.0498 - accuracy: 0.5359

10/10 [==============================] - 0s 19ms/step - loss: 1.0498 - accuracy: 0.5359 - val_loss: 1.0598 - val_accuracy: 0.4875
Epoch 33/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9951 - accuracy: 0.5156
10/10 [==============================] - 0s 2ms/step - loss: 1.0392 - accuracy: 0.5469

10/10 [==============================] - 0s 19ms/step - loss: 1.0392 - accuracy: 0.5469 - val_loss: 1.0506 - val_accuracy: 0.4938
Epoch 34/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0470 - accuracy: 0.4688
10/10 [==============================] - 0s 1ms/step - loss: 1.0288 - accuracy: 0.5516

10/10 [==============================] - 0s 21ms/step - loss: 1.0288 - accuracy: 0.5516 - val_loss: 1.0416 - val_accuracy: 0.4938
Epoch 35/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9138 - accuracy: 0.5781
10/10 [==============================] - 0s 1ms/step - loss: 1.0190 - accuracy: 0.5578

10/10 [==============================] - 0s 21ms/step - loss: 1.0190 - accuracy: 0.5578 - val_loss: 1.0329 - val_accuracy: 0.4938
Epoch 36/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0835 - accuracy: 0.5781
10/10 [==============================] - 0s 1ms/step - loss: 1.0091 - accuracy: 0.5625

10/10 [==============================] - 0s 19ms/step - loss: 1.0091 - accuracy: 0.5625 - val_loss: 1.0242 - val_accuracy: 0.5000
Epoch 37/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0256 - accuracy: 0.5156
10/10 [==============================] - 0s 1ms/step - loss: 0.9995 - accuracy: 0.5703

10/10 [==============================] - 0s 19ms/step - loss: 0.9995 - accuracy: 0.5703 - val_loss: 1.0155 - val_accuracy: 0.5125
Epoch 38/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0038 - accuracy: 0.6250
10/10 [==============================] - 0s 1ms/step - loss: 0.9902 - accuracy: 0.5766

10/10 [==============================] - 0s 21ms/step - loss: 0.9902 - accuracy: 0.5766 - val_loss: 1.0072 - val_accuracy: 0.5312
Epoch 39/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9574 - accuracy: 0.5938
10/10 [==============================] - 0s 1ms/step - loss: 0.9810 - accuracy: 0.5828

10/10 [==============================] - 0s 19ms/step - loss: 0.9810 - accuracy: 0.5828 - val_loss: 0.9988 - val_accuracy: 0.5375
Epoch 40/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8975 - accuracy: 0.7344
10/10 [==============================] - 0s 2ms/step - loss: 0.9721 - accuracy: 0.5984

10/10 [==============================] - 0s 19ms/step - loss: 0.9721 - accuracy: 0.5984 - val_loss: 0.9905 - val_accuracy: 0.5562
Epoch 41/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9671 - accuracy: 0.6094
10/10 [==============================] - 0s 2ms/step - loss: 0.9630 - accuracy: 0.6141

10/10 [==============================] - 0s 21ms/step - loss: 0.9630 - accuracy: 0.6141 - val_loss: 0.9825 - val_accuracy: 0.5750
Epoch 42/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0356 - accuracy: 0.6562
10/10 [==============================] - 0s 2ms/step - loss: 0.9544 - accuracy: 0.6406

10/10 [==============================] - 0s 19ms/step - loss: 0.9544 - accuracy: 0.6406 - val_loss: 0.9745 - val_accuracy: 0.5875
Epoch 43/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9289 - accuracy: 0.7188
10/10 [==============================] - 0s 1ms/step - loss: 0.9457 - accuracy: 0.6609

10/10 [==============================] - 0s 19ms/step - loss: 0.9457 - accuracy: 0.6609 - val_loss: 0.9666 - val_accuracy: 0.6062
Epoch 44/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9002 - accuracy: 0.7344
10/10 [==============================] - 0s 2ms/step - loss: 0.9373 - accuracy: 0.6766

10/10 [==============================] - 0s 20ms/step - loss: 0.9373 - accuracy: 0.6766 - val_loss: 0.9585 - val_accuracy: 0.6250
Epoch 45/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8786 - accuracy: 0.7344
10/10 [==============================] - 0s 1ms/step - loss: 0.9286 - accuracy: 0.6984

10/10 [==============================] - 0s 21ms/step - loss: 0.9286 - accuracy: 0.6984 - val_loss: 0.9506 - val_accuracy: 0.6438
Epoch 46/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9331 - accuracy: 0.6719
10/10 [==============================] - 0s 1ms/step - loss: 0.9202 - accuracy: 0.7172

10/10 [==============================] - 0s 19ms/step - loss: 0.9202 - accuracy: 0.7172 - val_loss: 0.9427 - val_accuracy: 0.6625
Epoch 47/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9868 - accuracy: 0.7188
10/10 [==============================] - 0s 2ms/step - loss: 0.9119 - accuracy: 0.7266

10/10 [==============================] - 0s 19ms/step - loss: 0.9119 - accuracy: 0.7266 - val_loss: 0.9347 - val_accuracy: 0.6938
Epoch 48/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8912 - accuracy: 0.7656
10/10 [==============================] - 0s 2ms/step - loss: 0.9035 - accuracy: 0.7312

10/10 [==============================] - 0s 21ms/step - loss: 0.9035 - accuracy: 0.7312 - val_loss: 0.9271 - val_accuracy: 0.7063
Epoch 49/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8103 - accuracy: 0.7969
10/10 [==============================] - 0s 1ms/step - loss: 0.8955 - accuracy: 0.7359

10/10 [==============================] - 0s 21ms/step - loss: 0.8955 - accuracy: 0.7359 - val_loss: 0.9192 - val_accuracy: 0.7063
Epoch 50/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8107 - accuracy: 0.7812
10/10 [==============================] - 0s 1ms/step - loss: 0.8873 - accuracy: 0.7406

10/10 [==============================] - 0s 21ms/step - loss: 0.8873 - accuracy: 0.7406 - val_loss: 0.9115 - val_accuracy: 0.7000
Epoch 51/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8005 - accuracy: 0.8125
10/10 [==============================] - 0s 1ms/step - loss: 0.8789 - accuracy: 0.7437

10/10 [==============================] - 0s 19ms/step - loss: 0.8789 - accuracy: 0.7437 - val_loss: 0.9038 - val_accuracy: 0.7125
Epoch 52/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8749 - accuracy: 0.7031
10/10 [==============================] - 0s 2ms/step - loss: 0.8712 - accuracy: 0.7437

10/10 [==============================] - 0s 21ms/step - loss: 0.8712 - accuracy: 0.7437 - val_loss: 0.8959 - val_accuracy: 0.7125
Epoch 53/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8146 - accuracy: 0.7344
10/10 [==============================] - 0s 1ms/step - loss: 0.8633 - accuracy: 0.7437

10/10 [==============================] - 0s 20ms/step - loss: 0.8633 - accuracy: 0.7437 - val_loss: 0.8881 - val_accuracy: 0.7125
Epoch 54/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8623 - accuracy: 0.6875
10/10 [==============================] - 0s 2ms/step - loss: 0.8556 - accuracy: 0.7422

10/10 [==============================] - 0s 19ms/step - loss: 0.8556 - accuracy: 0.7422 - val_loss: 0.8801 - val_accuracy: 0.7125
Epoch 55/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8356 - accuracy: 0.7188
10/10 [==============================] - 0s 1ms/step - loss: 0.8477 - accuracy: 0.7437

10/10 [==============================] - 0s 21ms/step - loss: 0.8477 - accuracy: 0.7437 - val_loss: 0.8726 - val_accuracy: 0.7125
Epoch 56/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8293 - accuracy: 0.7344
10/10 [==============================] - 0s 1ms/step - loss: 0.8402 - accuracy: 0.7437

10/10 [==============================] - 0s 19ms/step - loss: 0.8402 - accuracy: 0.7437 - val_loss: 0.8653 - val_accuracy: 0.7125
Epoch 57/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8253 - accuracy: 0.7812
10/10 [==============================] - 0s 1ms/step - loss: 0.8327 - accuracy: 0.7422

10/10 [==============================] - 0s 19ms/step - loss: 0.8327 - accuracy: 0.7422 - val_loss: 0.8577 - val_accuracy: 0.7188
Epoch 58/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7587 - accuracy: 0.7656
10/10 [==============================] - 0s 1ms/step - loss: 0.8256 - accuracy: 0.7422

10/10 [==============================] - 0s 19ms/step - loss: 0.8256 - accuracy: 0.7422 - val_loss: 0.8501 - val_accuracy: 0.7188
Epoch 59/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8123 - accuracy: 0.7500
10/10 [==============================] - 0s 1ms/step - loss: 0.8181 - accuracy: 0.7406

10/10 [==============================] - 0s 19ms/step - loss: 0.8181 - accuracy: 0.7406 - val_loss: 0.8433 - val_accuracy: 0.7188
Epoch 60/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7829 - accuracy: 0.7344
10/10 [==============================] - 0s 1ms/step - loss: 0.8113 - accuracy: 0.7375

10/10 [==============================] - 0s 21ms/step - loss: 0.8113 - accuracy: 0.7375 - val_loss: 0.8362 - val_accuracy: 0.7188
Epoch 61/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7122 - accuracy: 0.8125
10/10 [==============================] - 0s 1ms/step - loss: 0.8045 - accuracy: 0.7375

10/10 [==============================] - 0s 19ms/step - loss: 0.8045 - accuracy: 0.7375 - val_loss: 0.8293 - val_accuracy: 0.7188
Epoch 62/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7523 - accuracy: 0.7812
10/10 [==============================] - 0s 2ms/step - loss: 0.7979 - accuracy: 0.7375

10/10 [==============================] - 0s 19ms/step - loss: 0.7979 - accuracy: 0.7375 - val_loss: 0.8224 - val_accuracy: 0.7188
Epoch 63/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7859 - accuracy: 0.7031
10/10 [==============================] - 0s 3ms/step - loss: 0.7913 - accuracy: 0.7391

10/10 [==============================] - 0s 20ms/step - loss: 0.7913 - accuracy: 0.7391 - val_loss: 0.8157 - val_accuracy: 0.7125
Epoch 64/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7435 - accuracy: 0.7969
10/10 [==============================] - 0s 1ms/step - loss: 0.7852 - accuracy: 0.7406

10/10 [==============================] - 0s 21ms/step - loss: 0.7852 - accuracy: 0.7406 - val_loss: 0.8092 - val_accuracy: 0.7188
Epoch 65/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6159 - accuracy: 0.8750
10/10 [==============================] - 0s 1ms/step - loss: 0.7792 - accuracy: 0.7422

10/10 [==============================] - 0s 20ms/step - loss: 0.7792 - accuracy: 0.7422 - val_loss: 0.8030 - val_accuracy: 0.7250
Epoch 66/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7768 - accuracy: 0.7344
10/10 [==============================] - 0s 2ms/step - loss: 0.7733 - accuracy: 0.7437

10/10 [==============================] - 0s 21ms/step - loss: 0.7733 - accuracy: 0.7437 - val_loss: 0.7971 - val_accuracy: 0.7250
Epoch 67/100

 1/10 [==>...........................] - ETA: 0s - loss: 1.0378 - accuracy: 0.6250
10/10 [==============================] - 0s 1ms/step - loss: 0.7677 - accuracy: 0.7437

10/10 [==============================] - 0s 24ms/step - loss: 0.7677 - accuracy: 0.7437 - val_loss: 0.7915 - val_accuracy: 0.7250
Epoch 68/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8602 - accuracy: 0.6875
10/10 [==============================] - 0s 1ms/step - loss: 0.7623 - accuracy: 0.7453

10/10 [==============================] - 0s 19ms/step - loss: 0.7623 - accuracy: 0.7453 - val_loss: 0.7861 - val_accuracy: 0.7250
Epoch 69/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9088 - accuracy: 0.6094
10/10 [==============================] - 0s 2ms/step - loss: 0.7573 - accuracy: 0.7453

10/10 [==============================] - 0s 21ms/step - loss: 0.7573 - accuracy: 0.7453 - val_loss: 0.7808 - val_accuracy: 0.7250
Epoch 70/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7134 - accuracy: 0.7500
10/10 [==============================] - 0s 1ms/step - loss: 0.7522 - accuracy: 0.7437

10/10 [==============================] - 0s 19ms/step - loss: 0.7522 - accuracy: 0.7437 - val_loss: 0.7758 - val_accuracy: 0.7250
Epoch 71/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8637 - accuracy: 0.6562
10/10 [==============================] - 0s 1ms/step - loss: 0.7472 - accuracy: 0.7422

10/10 [==============================] - 0s 21ms/step - loss: 0.7472 - accuracy: 0.7422 - val_loss: 0.7705 - val_accuracy: 0.7250
Epoch 72/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8050 - accuracy: 0.7031
10/10 [==============================] - 0s 2ms/step - loss: 0.7427 - accuracy: 0.7422

10/10 [==============================] - 0s 20ms/step - loss: 0.7427 - accuracy: 0.7422 - val_loss: 0.7656 - val_accuracy: 0.7250
Epoch 73/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7248 - accuracy: 0.7812
10/10 [==============================] - 0s 2ms/step - loss: 0.7380 - accuracy: 0.7422

10/10 [==============================] - 0s 19ms/step - loss: 0.7380 - accuracy: 0.7422 - val_loss: 0.7610 - val_accuracy: 0.7250
Epoch 74/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6960 - accuracy: 0.7188
10/10 [==============================] - 0s 1ms/step - loss: 0.7338 - accuracy: 0.7437

10/10 [==============================] - 0s 21ms/step - loss: 0.7338 - accuracy: 0.7437 - val_loss: 0.7566 - val_accuracy: 0.7312
Epoch 75/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7912 - accuracy: 0.7656
10/10 [==============================] - 0s 2ms/step - loss: 0.7295 - accuracy: 0.7469

10/10 [==============================] - 0s 19ms/step - loss: 0.7295 - accuracy: 0.7469 - val_loss: 0.7523 - val_accuracy: 0.7312
Epoch 76/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7076 - accuracy: 0.7188
10/10 [==============================] - 0s 1ms/step - loss: 0.7256 - accuracy: 0.7469

10/10 [==============================] - 0s 19ms/step - loss: 0.7256 - accuracy: 0.7469 - val_loss: 0.7478 - val_accuracy: 0.7312
Epoch 77/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8040 - accuracy: 0.8125
10/10 [==============================] - 0s 1ms/step - loss: 0.7216 - accuracy: 0.7484

10/10 [==============================] - 0s 20ms/step - loss: 0.7216 - accuracy: 0.7484 - val_loss: 0.7440 - val_accuracy: 0.7312
Epoch 78/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7015 - accuracy: 0.7969
10/10 [==============================] - 0s 1ms/step - loss: 0.7179 - accuracy: 0.7516

10/10 [==============================] - 0s 19ms/step - loss: 0.7179 - accuracy: 0.7516 - val_loss: 0.7402 - val_accuracy: 0.7312
Epoch 79/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8034 - accuracy: 0.6406
10/10 [==============================] - 0s 1ms/step - loss: 0.7143 - accuracy: 0.7531

10/10 [==============================] - 0s 19ms/step - loss: 0.7143 - accuracy: 0.7531 - val_loss: 0.7364 - val_accuracy: 0.7375
Epoch 80/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6254 - accuracy: 0.7656
10/10 [==============================] - 0s 1ms/step - loss: 0.7107 - accuracy: 0.7531

10/10 [==============================] - 0s 21ms/step - loss: 0.7107 - accuracy: 0.7531 - val_loss: 0.7329 - val_accuracy: 0.7375
Epoch 81/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7109 - accuracy: 0.7656
10/10 [==============================] - 0s 2ms/step - loss: 0.7074 - accuracy: 0.7531

10/10 [==============================] - 0s 21ms/step - loss: 0.7074 - accuracy: 0.7531 - val_loss: 0.7296 - val_accuracy: 0.7375
Epoch 82/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6965 - accuracy: 0.7812
10/10 [==============================] - 0s 1ms/step - loss: 0.7043 - accuracy: 0.7531

10/10 [==============================] - 0s 21ms/step - loss: 0.7043 - accuracy: 0.7531 - val_loss: 0.7261 - val_accuracy: 0.7437
Epoch 83/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.5485 - accuracy: 0.8281
10/10 [==============================] - 0s 1ms/step - loss: 0.7010 - accuracy: 0.7531

10/10 [==============================] - 0s 21ms/step - loss: 0.7010 - accuracy: 0.7531 - val_loss: 0.7230 - val_accuracy: 0.7437
Epoch 84/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6828 - accuracy: 0.7344
10/10 [==============================] - 0s 1ms/step - loss: 0.6979 - accuracy: 0.7516

10/10 [==============================] - 0s 19ms/step - loss: 0.6979 - accuracy: 0.7516 - val_loss: 0.7200 - val_accuracy: 0.7375
Epoch 85/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.9560 - accuracy: 0.6719
10/10 [==============================] - 0s 2ms/step - loss: 0.6949 - accuracy: 0.7516

10/10 [==============================] - 0s 21ms/step - loss: 0.6949 - accuracy: 0.7516 - val_loss: 0.7167 - val_accuracy: 0.7375
Epoch 86/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8000 - accuracy: 0.6719
10/10 [==============================] - 0s 1ms/step - loss: 0.6921 - accuracy: 0.7531

10/10 [==============================] - 0s 19ms/step - loss: 0.6921 - accuracy: 0.7531 - val_loss: 0.7138 - val_accuracy: 0.7250
Epoch 87/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6808 - accuracy: 0.7656
10/10 [==============================] - 0s 1ms/step - loss: 0.6893 - accuracy: 0.7547

10/10 [==============================] - 0s 21ms/step - loss: 0.6893 - accuracy: 0.7547 - val_loss: 0.7110 - val_accuracy: 0.7250
Epoch 88/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7179 - accuracy: 0.7188
10/10 [==============================] - 0s 1ms/step - loss: 0.6867 - accuracy: 0.7484

10/10 [==============================] - 0s 19ms/step - loss: 0.6867 - accuracy: 0.7484 - val_loss: 0.7082 - val_accuracy: 0.7375
Epoch 89/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6161 - accuracy: 0.7344
10/10 [==============================] - 0s 1ms/step - loss: 0.6840 - accuracy: 0.7500

10/10 [==============================] - 0s 20ms/step - loss: 0.6840 - accuracy: 0.7500 - val_loss: 0.7054 - val_accuracy: 0.7375
Epoch 90/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.5837 - accuracy: 0.7812
10/10 [==============================] - 0s 1ms/step - loss: 0.6815 - accuracy: 0.7531

10/10 [==============================] - 0s 19ms/step - loss: 0.6815 - accuracy: 0.7531 - val_loss: 0.7029 - val_accuracy: 0.7312
Epoch 91/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.8174 - accuracy: 0.7969
10/10 [==============================] - 0s 1ms/step - loss: 0.6790 - accuracy: 0.7531

10/10 [==============================] - 0s 45ms/step - loss: 0.6790 - accuracy: 0.7531 - val_loss: 0.7004 - val_accuracy: 0.7375
Epoch 92/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6039 - accuracy: 0.7812
10/10 [==============================] - 0s 2ms/step - loss: 0.6766 - accuracy: 0.7516

10/10 [==============================] - 0s 20ms/step - loss: 0.6766 - accuracy: 0.7516 - val_loss: 0.6981 - val_accuracy: 0.7312
Epoch 93/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.7240 - accuracy: 0.7344
10/10 [==============================] - 0s 997us/step - loss: 0.6742 - accuracy: 0.7531

10/10 [==============================] - 0s 17ms/step - loss: 0.6742 - accuracy: 0.7531 - val_loss: 0.6955 - val_accuracy: 0.7250
Epoch 94/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.5579 - accuracy: 0.8125
10/10 [==============================] - 0s 2ms/step - loss: 0.6720 - accuracy: 0.7531

10/10 [==============================] - 0s 19ms/step - loss: 0.6720 - accuracy: 0.7531 - val_loss: 0.6933 - val_accuracy: 0.7250
Epoch 95/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6867 - accuracy: 0.6719
10/10 [==============================] - 0s 1ms/step - loss: 0.6697 - accuracy: 0.7531

10/10 [==============================] - 0s 19ms/step - loss: 0.6697 - accuracy: 0.7531 - val_loss: 0.6913 - val_accuracy: 0.7250
Epoch 96/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.5171 - accuracy: 0.8281
10/10 [==============================] - 0s 2ms/step - loss: 0.6676 - accuracy: 0.7531

10/10 [==============================] - 0s 21ms/step - loss: 0.6676 - accuracy: 0.7531 - val_loss: 0.6893 - val_accuracy: 0.7312
Epoch 97/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6621 - accuracy: 0.7812
10/10 [==============================] - 0s 1ms/step - loss: 0.6658 - accuracy: 0.7547

10/10 [==============================] - 0s 21ms/step - loss: 0.6658 - accuracy: 0.7547 - val_loss: 0.6868 - val_accuracy: 0.7250
Epoch 98/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.6414 - accuracy: 0.7031
10/10 [==============================] - 0s 2ms/step - loss: 0.6633 - accuracy: 0.7563

10/10 [==============================] - 0s 21ms/step - loss: 0.6633 - accuracy: 0.7563 - val_loss: 0.6849 - val_accuracy: 0.7250
Epoch 99/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.5862 - accuracy: 0.8281
10/10 [==============================] - 0s 1ms/step - loss: 0.6614 - accuracy: 0.7547

10/10 [==============================] - 0s 19ms/step - loss: 0.6614 - accuracy: 0.7547 - val_loss: 0.6827 - val_accuracy: 0.7250
Epoch 100/100

 1/10 [==>...........................] - ETA: 0s - loss: 0.5876 - accuracy: 0.8438
10/10 [==============================] - 0s 2ms/step - loss: 0.6594 - accuracy: 0.7578

10/10 [==============================] - 0s 21ms/step - loss: 0.6594 - accuracy: 0.7578 - val_loss: 0.6808 - val_accuracy: 0.7250

end <- Sys.time()

runtime.64 <- round(as.numeric(difftime(time1 = end, time2 = begin, units = "secs")), 2)
  
pred.prob <- predict(model.64, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.64 <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# Batch 128
model.128 <- NULL
begin <- Sys.time()
model.128 <- create_model(128)

Epoch 1/100

1/5 [=====>........................] - ETA: 1s - loss: 1.2795 - accuracy: 0.3672
5/5 [==============================] - 0s 1ms/step - loss: 1.2632 - accuracy: 0.3891

5/5 [==============================] - 1s 168ms/step - loss: 1.2632 - accuracy: 0.3891 - val_loss: 1.2923 - val_accuracy: 0.3750
Epoch 2/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2275 - accuracy: 0.4453
5/5 [==============================] - 0s 3ms/step - loss: 1.2517 - accuracy: 0.3953

5/5 [==============================] - 0s 41ms/step - loss: 1.2517 - accuracy: 0.3953 - val_loss: 1.2804 - val_accuracy: 0.3875
Epoch 3/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2470 - accuracy: 0.3828
5/5 [==============================] - 0s 1ms/step - loss: 1.2416 - accuracy: 0.3969

5/5 [==============================] - 0s 43ms/step - loss: 1.2416 - accuracy: 0.3969 - val_loss: 1.2685 - val_accuracy: 0.3875
Epoch 4/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2803 - accuracy: 0.3438
5/5 [==============================] - 0s 2ms/step - loss: 1.2312 - accuracy: 0.3953

5/5 [==============================] - 0s 39ms/step - loss: 1.2312 - accuracy: 0.3953 - val_loss: 1.2570 - val_accuracy: 0.3875
Epoch 5/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2308 - accuracy: 0.4453
5/5 [==============================] - 0s 2ms/step - loss: 1.2212 - accuracy: 0.3984

5/5 [==============================] - 0s 42ms/step - loss: 1.2212 - accuracy: 0.3984 - val_loss: 1.2458 - val_accuracy: 0.3875
Epoch 6/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2150 - accuracy: 0.4141
5/5 [==============================] - 0s 1ms/step - loss: 1.2114 - accuracy: 0.4031

5/5 [==============================] - 0s 42ms/step - loss: 1.2114 - accuracy: 0.4031 - val_loss: 1.2349 - val_accuracy: 0.3938
Epoch 7/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2225 - accuracy: 0.4141
5/5 [==============================] - 0s 1ms/step - loss: 1.2019 - accuracy: 0.4078

5/5 [==============================] - 0s 43ms/step - loss: 1.2019 - accuracy: 0.4078 - val_loss: 1.2244 - val_accuracy: 0.3938
Epoch 8/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2195 - accuracy: 0.4062
5/5 [==============================] - 0s 2ms/step - loss: 1.1930 - accuracy: 0.4094

5/5 [==============================] - 0s 42ms/step - loss: 1.1930 - accuracy: 0.4094 - val_loss: 1.2140 - val_accuracy: 0.3938
Epoch 9/100

1/5 [=====>........................] - ETA: 0s - loss: 1.2147 - accuracy: 0.3359
5/5 [==============================] - 0s 1ms/step - loss: 1.1840 - accuracy: 0.4141

5/5 [==============================] - 0s 43ms/step - loss: 1.1840 - accuracy: 0.4141 - val_loss: 1.2039 - val_accuracy: 0.3875
Epoch 10/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1764 - accuracy: 0.4453
5/5 [==============================] - 0s 1ms/step - loss: 1.1748 - accuracy: 0.4125

5/5 [==============================] - 0s 43ms/step - loss: 1.1748 - accuracy: 0.4125 - val_loss: 1.1942 - val_accuracy: 0.4000
Epoch 11/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1623 - accuracy: 0.4766
5/5 [==============================] - 0s 2ms/step - loss: 1.1661 - accuracy: 0.4234

5/5 [==============================] - 0s 42ms/step - loss: 1.1661 - accuracy: 0.4234 - val_loss: 1.1846 - val_accuracy: 0.4062
Epoch 12/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1528 - accuracy: 0.3984
5/5 [==============================] - 0s 1ms/step - loss: 1.1576 - accuracy: 0.4297

5/5 [==============================] - 0s 51ms/step - loss: 1.1576 - accuracy: 0.4297 - val_loss: 1.1751 - val_accuracy: 0.4187
Epoch 13/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1930 - accuracy: 0.4141
5/5 [==============================] - 0s 2ms/step - loss: 1.1491 - accuracy: 0.4359

5/5 [==============================] - 0s 42ms/step - loss: 1.1491 - accuracy: 0.4359 - val_loss: 1.1657 - val_accuracy: 0.4062
Epoch 14/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1446 - accuracy: 0.3984
5/5 [==============================] - 0s 1ms/step - loss: 1.1407 - accuracy: 0.4453

5/5 [==============================] - 0s 47ms/step - loss: 1.1407 - accuracy: 0.4453 - val_loss: 1.1566 - val_accuracy: 0.4062
Epoch 15/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1595 - accuracy: 0.4297
5/5 [==============================] - 0s 2ms/step - loss: 1.1322 - accuracy: 0.4563

5/5 [==============================] - 0s 47ms/step - loss: 1.1322 - accuracy: 0.4563 - val_loss: 1.1476 - val_accuracy: 0.4250
Epoch 16/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1287 - accuracy: 0.4375
5/5 [==============================] - 0s 2ms/step - loss: 1.1240 - accuracy: 0.4703

5/5 [==============================] - 0s 45ms/step - loss: 1.1240 - accuracy: 0.4703 - val_loss: 1.1388 - val_accuracy: 0.4563
Epoch 17/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1140 - accuracy: 0.4453
5/5 [==============================] - 0s 2ms/step - loss: 1.1156 - accuracy: 0.5016

5/5 [==============================] - 0s 46ms/step - loss: 1.1156 - accuracy: 0.5016 - val_loss: 1.1300 - val_accuracy: 0.4688
Epoch 18/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1261 - accuracy: 0.5000
5/5 [==============================] - 0s 2ms/step - loss: 1.1072 - accuracy: 0.5953

5/5 [==============================] - 0s 42ms/step - loss: 1.1072 - accuracy: 0.5953 - val_loss: 1.1214 - val_accuracy: 0.5813
Epoch 19/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1071 - accuracy: 0.6875
5/5 [==============================] - 0s 2ms/step - loss: 1.0990 - accuracy: 0.6828

5/5 [==============================] - 0s 49ms/step - loss: 1.0990 - accuracy: 0.6828 - val_loss: 1.1129 - val_accuracy: 0.6313
Epoch 20/100

1/5 [=====>........................] - ETA: 0s - loss: 1.1575 - accuracy: 0.7188
5/5 [==============================] - 0s 2ms/step - loss: 1.0907 - accuracy: 0.6953

5/5 [==============================] - 0s 43ms/step - loss: 1.0907 - accuracy: 0.6953 - val_loss: 1.1042 - val_accuracy: 0.6438
Epoch 21/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0753 - accuracy: 0.7344
5/5 [==============================] - 0s 2ms/step - loss: 1.0823 - accuracy: 0.7000

5/5 [==============================] - 0s 43ms/step - loss: 1.0823 - accuracy: 0.7000 - val_loss: 1.0958 - val_accuracy: 0.6438
Epoch 22/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0726 - accuracy: 0.7031
5/5 [==============================] - 0s 2ms/step - loss: 1.0740 - accuracy: 0.7031

5/5 [==============================] - 0s 42ms/step - loss: 1.0740 - accuracy: 0.7031 - val_loss: 1.0874 - val_accuracy: 0.6562
Epoch 23/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0511 - accuracy: 0.7266
5/5 [==============================] - 0s 1ms/step - loss: 1.0660 - accuracy: 0.7078

5/5 [==============================] - 0s 42ms/step - loss: 1.0660 - accuracy: 0.7078 - val_loss: 1.0790 - val_accuracy: 0.6438
Epoch 24/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0280 - accuracy: 0.7266
5/5 [==============================] - 0s 1ms/step - loss: 1.0577 - accuracy: 0.7047

5/5 [==============================] - 0s 43ms/step - loss: 1.0577 - accuracy: 0.7047 - val_loss: 1.0706 - val_accuracy: 0.6500
Epoch 25/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0844 - accuracy: 0.6406
5/5 [==============================] - 0s 1ms/step - loss: 1.0495 - accuracy: 0.6984

5/5 [==============================] - 0s 42ms/step - loss: 1.0495 - accuracy: 0.6984 - val_loss: 1.0621 - val_accuracy: 0.6375
Epoch 26/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0410 - accuracy: 0.7344
5/5 [==============================] - 0s 2ms/step - loss: 1.0413 - accuracy: 0.6984

5/5 [==============================] - 0s 42ms/step - loss: 1.0413 - accuracy: 0.6984 - val_loss: 1.0539 - val_accuracy: 0.6313
Epoch 27/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0807 - accuracy: 0.6719
5/5 [==============================] - 0s 2ms/step - loss: 1.0333 - accuracy: 0.6984

5/5 [==============================] - 0s 39ms/step - loss: 1.0333 - accuracy: 0.6984 - val_loss: 1.0456 - val_accuracy: 0.6438
Epoch 28/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0401 - accuracy: 0.6953
5/5 [==============================] - 0s 2ms/step - loss: 1.0251 - accuracy: 0.6984

5/5 [==============================] - 0s 43ms/step - loss: 1.0251 - accuracy: 0.6984 - val_loss: 1.0374 - val_accuracy: 0.6438
Epoch 29/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9999 - accuracy: 0.7422
5/5 [==============================] - 0s 2ms/step - loss: 1.0170 - accuracy: 0.7078

5/5 [==============================] - 0s 43ms/step - loss: 1.0170 - accuracy: 0.7078 - val_loss: 1.0293 - val_accuracy: 0.6562
Epoch 30/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0370 - accuracy: 0.7266
5/5 [==============================] - 0s 2ms/step - loss: 1.0092 - accuracy: 0.7063

5/5 [==============================] - 0s 42ms/step - loss: 1.0092 - accuracy: 0.7063 - val_loss: 1.0213 - val_accuracy: 0.6500
Epoch 31/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0324 - accuracy: 0.6953
5/5 [==============================] - 0s 2ms/step - loss: 1.0012 - accuracy: 0.6969

5/5 [==============================] - 0s 43ms/step - loss: 1.0012 - accuracy: 0.6969 - val_loss: 1.0132 - val_accuracy: 0.6438
Epoch 32/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0675 - accuracy: 0.6484
5/5 [==============================] - 0s 2ms/step - loss: 0.9930 - accuracy: 0.6969

5/5 [==============================] - 0s 46ms/step - loss: 0.9930 - accuracy: 0.6969 - val_loss: 1.0054 - val_accuracy: 0.6375
Epoch 33/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9945 - accuracy: 0.6484
5/5 [==============================] - 0s 3ms/step - loss: 0.9852 - accuracy: 0.6969

5/5 [==============================] - 0s 46ms/step - loss: 0.9852 - accuracy: 0.6969 - val_loss: 0.9977 - val_accuracy: 0.6375
Epoch 34/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0593 - accuracy: 0.6406
5/5 [==============================] - 0s 2ms/step - loss: 0.9773 - accuracy: 0.6953

5/5 [==============================] - 0s 46ms/step - loss: 0.9773 - accuracy: 0.6953 - val_loss: 0.9901 - val_accuracy: 0.6250
Epoch 35/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9897 - accuracy: 0.6953
5/5 [==============================] - 0s 1ms/step - loss: 0.9696 - accuracy: 0.6953

5/5 [==============================] - 0s 42ms/step - loss: 0.9696 - accuracy: 0.6953 - val_loss: 0.9825 - val_accuracy: 0.6250
Epoch 36/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9492 - accuracy: 0.6719
5/5 [==============================] - 0s 3ms/step - loss: 0.9619 - accuracy: 0.6984

5/5 [==============================] - 0s 46ms/step - loss: 0.9619 - accuracy: 0.6984 - val_loss: 0.9751 - val_accuracy: 0.6250
Epoch 37/100

1/5 [=====>........................] - ETA: 0s - loss: 1.0023 - accuracy: 0.6719
5/5 [==============================] - 0s 2ms/step - loss: 0.9543 - accuracy: 0.6953

5/5 [==============================] - 0s 43ms/step - loss: 0.9543 - accuracy: 0.6953 - val_loss: 0.9679 - val_accuracy: 0.6250
Epoch 38/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9453 - accuracy: 0.7109
5/5 [==============================] - 0s 2ms/step - loss: 0.9469 - accuracy: 0.7000

5/5 [==============================] - 0s 47ms/step - loss: 0.9469 - accuracy: 0.7000 - val_loss: 0.9609 - val_accuracy: 0.6187
Epoch 39/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9612 - accuracy: 0.7031
5/5 [==============================] - 0s 2ms/step - loss: 0.9396 - accuracy: 0.7031

5/5 [==============================] - 0s 42ms/step - loss: 0.9396 - accuracy: 0.7031 - val_loss: 0.9540 - val_accuracy: 0.6313
Epoch 40/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9311 - accuracy: 0.7031
5/5 [==============================] - 0s 1ms/step - loss: 0.9321 - accuracy: 0.7016

5/5 [==============================] - 0s 42ms/step - loss: 0.9321 - accuracy: 0.7016 - val_loss: 0.9473 - val_accuracy: 0.6250
Epoch 41/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8814 - accuracy: 0.7344
5/5 [==============================] - 0s 1ms/step - loss: 0.9252 - accuracy: 0.7063

5/5 [==============================] - 0s 43ms/step - loss: 0.9252 - accuracy: 0.7063 - val_loss: 0.9406 - val_accuracy: 0.6313
Epoch 42/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9361 - accuracy: 0.6953
5/5 [==============================] - 0s 1ms/step - loss: 0.9181 - accuracy: 0.7094

5/5 [==============================] - 0s 47ms/step - loss: 0.9181 - accuracy: 0.7094 - val_loss: 0.9342 - val_accuracy: 0.6375
Epoch 43/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8924 - accuracy: 0.6875
5/5 [==============================] - 0s 2ms/step - loss: 0.9113 - accuracy: 0.7109

5/5 [==============================] - 0s 42ms/step - loss: 0.9113 - accuracy: 0.7109 - val_loss: 0.9280 - val_accuracy: 0.6500
Epoch 44/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9441 - accuracy: 0.6641
5/5 [==============================] - 0s 2ms/step - loss: 0.9048 - accuracy: 0.7125

5/5 [==============================] - 0s 43ms/step - loss: 0.9048 - accuracy: 0.7125 - val_loss: 0.9219 - val_accuracy: 0.6750
Epoch 45/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9053 - accuracy: 0.6875
5/5 [==============================] - 0s 1ms/step - loss: 0.8984 - accuracy: 0.7156

5/5 [==============================] - 0s 47ms/step - loss: 0.8984 - accuracy: 0.7156 - val_loss: 0.9160 - val_accuracy: 0.6750
Epoch 46/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8387 - accuracy: 0.7656
5/5 [==============================] - 0s 1ms/step - loss: 0.8923 - accuracy: 0.7125

5/5 [==============================] - 0s 43ms/step - loss: 0.8923 - accuracy: 0.7125 - val_loss: 0.9102 - val_accuracy: 0.6687
Epoch 47/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9671 - accuracy: 0.6875
5/5 [==============================] - 0s 1ms/step - loss: 0.8862 - accuracy: 0.7125

5/5 [==============================] - 0s 42ms/step - loss: 0.8862 - accuracy: 0.7125 - val_loss: 0.9046 - val_accuracy: 0.6687
Epoch 48/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8883 - accuracy: 0.6875
5/5 [==============================] - 0s 2ms/step - loss: 0.8804 - accuracy: 0.7141

5/5 [==============================] - 0s 42ms/step - loss: 0.8804 - accuracy: 0.7141 - val_loss: 0.8992 - val_accuracy: 0.6750
Epoch 49/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8215 - accuracy: 0.8047
5/5 [==============================] - 0s 3ms/step - loss: 0.8748 - accuracy: 0.7188

5/5 [==============================] - 0s 47ms/step - loss: 0.8748 - accuracy: 0.7188 - val_loss: 0.8940 - val_accuracy: 0.6750
Epoch 50/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8815 - accuracy: 0.7188
5/5 [==============================] - 0s 3ms/step - loss: 0.8690 - accuracy: 0.7203

5/5 [==============================] - 0s 46ms/step - loss: 0.8690 - accuracy: 0.7203 - val_loss: 0.8890 - val_accuracy: 0.6750
Epoch 51/100

1/5 [=====>........................] - ETA: 0s - loss: 0.9513 - accuracy: 0.7031
5/5 [==============================] - 0s 1ms/step - loss: 0.8638 - accuracy: 0.7172

5/5 [==============================] - 0s 43ms/step - loss: 0.8638 - accuracy: 0.7172 - val_loss: 0.8842 - val_accuracy: 0.6687
Epoch 52/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8570 - accuracy: 0.6875
5/5 [==============================] - 0s 3ms/step - loss: 0.8586 - accuracy: 0.7172

5/5 [==============================] - 0s 50ms/step - loss: 0.8586 - accuracy: 0.7172 - val_loss: 0.8794 - val_accuracy: 0.6687
Epoch 53/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8213 - accuracy: 0.6953
5/5 [==============================] - 0s 3ms/step - loss: 0.8537 - accuracy: 0.7156

5/5 [==============================] - 0s 42ms/step - loss: 0.8537 - accuracy: 0.7156 - val_loss: 0.8746 - val_accuracy: 0.6687
Epoch 54/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8140 - accuracy: 0.7422
5/5 [==============================] - 0s 2ms/step - loss: 0.8488 - accuracy: 0.7156

5/5 [==============================] - 0s 46ms/step - loss: 0.8488 - accuracy: 0.7156 - val_loss: 0.8702 - val_accuracy: 0.6687
Epoch 55/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8534 - accuracy: 0.6641
5/5 [==============================] - 0s 1ms/step - loss: 0.8439 - accuracy: 0.7172

5/5 [==============================] - 0s 46ms/step - loss: 0.8439 - accuracy: 0.7172 - val_loss: 0.8658 - val_accuracy: 0.6750
Epoch 56/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8214 - accuracy: 0.7266
5/5 [==============================] - 0s 1ms/step - loss: 0.8394 - accuracy: 0.7234

5/5 [==============================] - 0s 46ms/step - loss: 0.8394 - accuracy: 0.7234 - val_loss: 0.8615 - val_accuracy: 0.6750
Epoch 57/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8356 - accuracy: 0.7422
5/5 [==============================] - 0s 2ms/step - loss: 0.8350 - accuracy: 0.7203

5/5 [==============================] - 0s 42ms/step - loss: 0.8350 - accuracy: 0.7203 - val_loss: 0.8574 - val_accuracy: 0.6750
Epoch 58/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8088 - accuracy: 0.7266
5/5 [==============================] - 0s 1ms/step - loss: 0.8307 - accuracy: 0.7234

5/5 [==============================] - 0s 43ms/step - loss: 0.8307 - accuracy: 0.7234 - val_loss: 0.8535 - val_accuracy: 0.6750
Epoch 59/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8619 - accuracy: 0.7031
5/5 [==============================] - 0s 2ms/step - loss: 0.8265 - accuracy: 0.7203

5/5 [==============================] - 0s 43ms/step - loss: 0.8265 - accuracy: 0.7203 - val_loss: 0.8496 - val_accuracy: 0.6750
Epoch 60/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7241 - accuracy: 0.7812
5/5 [==============================] - 0s 2ms/step - loss: 0.8224 - accuracy: 0.7234

5/5 [==============================] - 0s 43ms/step - loss: 0.8224 - accuracy: 0.7234 - val_loss: 0.8459 - val_accuracy: 0.6812
Epoch 61/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8547 - accuracy: 0.6719
5/5 [==============================] - 0s 1ms/step - loss: 0.8183 - accuracy: 0.7188

5/5 [==============================] - 0s 42ms/step - loss: 0.8183 - accuracy: 0.7188 - val_loss: 0.8421 - val_accuracy: 0.6812
Epoch 62/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8071 - accuracy: 0.6719
5/5 [==============================] - 0s 1ms/step - loss: 0.8145 - accuracy: 0.7219

5/5 [==============================] - 0s 43ms/step - loss: 0.8145 - accuracy: 0.7219 - val_loss: 0.8384 - val_accuracy: 0.6875
Epoch 63/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7791 - accuracy: 0.7422
5/5 [==============================] - 0s 1ms/step - loss: 0.8107 - accuracy: 0.7266

5/5 [==============================] - 0s 42ms/step - loss: 0.8107 - accuracy: 0.7266 - val_loss: 0.8348 - val_accuracy: 0.6938
Epoch 64/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8005 - accuracy: 0.7656
5/5 [==============================] - 0s 2ms/step - loss: 0.8071 - accuracy: 0.7297

5/5 [==============================] - 0s 43ms/step - loss: 0.8071 - accuracy: 0.7297 - val_loss: 0.8311 - val_accuracy: 0.7000
Epoch 65/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8085 - accuracy: 0.7422
5/5 [==============================] - 0s 1ms/step - loss: 0.8036 - accuracy: 0.7312

5/5 [==============================] - 0s 42ms/step - loss: 0.8036 - accuracy: 0.7312 - val_loss: 0.8279 - val_accuracy: 0.7000
Epoch 66/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8359 - accuracy: 0.7109
5/5 [==============================] - 0s 2ms/step - loss: 0.8001 - accuracy: 0.7281

5/5 [==============================] - 0s 47ms/step - loss: 0.8001 - accuracy: 0.7281 - val_loss: 0.8245 - val_accuracy: 0.7063
Epoch 67/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7641 - accuracy: 0.7656
5/5 [==============================] - 0s 1ms/step - loss: 0.7966 - accuracy: 0.7312

5/5 [==============================] - 0s 44ms/step - loss: 0.7966 - accuracy: 0.7312 - val_loss: 0.8213 - val_accuracy: 0.7125
Epoch 68/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8683 - accuracy: 0.6797
5/5 [==============================] - 0s 2ms/step - loss: 0.7934 - accuracy: 0.7312

5/5 [==============================] - 0s 42ms/step - loss: 0.7934 - accuracy: 0.7312 - val_loss: 0.8181 - val_accuracy: 0.7000
Epoch 69/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7664 - accuracy: 0.7969
5/5 [==============================] - 0s 1ms/step - loss: 0.7902 - accuracy: 0.7328

5/5 [==============================] - 0s 47ms/step - loss: 0.7902 - accuracy: 0.7328 - val_loss: 0.8149 - val_accuracy: 0.7125
Epoch 70/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7413 - accuracy: 0.7734
5/5 [==============================] - 0s 3ms/step - loss: 0.7870 - accuracy: 0.7344

5/5 [==============================] - 0s 45ms/step - loss: 0.7870 - accuracy: 0.7344 - val_loss: 0.8119 - val_accuracy: 0.7063
Epoch 71/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7430 - accuracy: 0.7578
5/5 [==============================] - 0s 2ms/step - loss: 0.7839 - accuracy: 0.7328

5/5 [==============================] - 0s 47ms/step - loss: 0.7839 - accuracy: 0.7328 - val_loss: 0.8090 - val_accuracy: 0.7063
Epoch 72/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7663 - accuracy: 0.7266
5/5 [==============================] - 0s 2ms/step - loss: 0.7808 - accuracy: 0.7297

5/5 [==============================] - 0s 54ms/step - loss: 0.7808 - accuracy: 0.7297 - val_loss: 0.8063 - val_accuracy: 0.7125
Epoch 73/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7923 - accuracy: 0.7734
5/5 [==============================] - 0s 1ms/step - loss: 0.7779 - accuracy: 0.7312

5/5 [==============================] - 0s 46ms/step - loss: 0.7779 - accuracy: 0.7312 - val_loss: 0.8035 - val_accuracy: 0.7125
Epoch 74/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7680 - accuracy: 0.7344
5/5 [==============================] - 0s 2ms/step - loss: 0.7750 - accuracy: 0.7312

5/5 [==============================] - 0s 39ms/step - loss: 0.7750 - accuracy: 0.7312 - val_loss: 0.8007 - val_accuracy: 0.7188
Epoch 75/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7290 - accuracy: 0.7734
5/5 [==============================] - 0s 2ms/step - loss: 0.7721 - accuracy: 0.7344

5/5 [==============================] - 0s 46ms/step - loss: 0.7721 - accuracy: 0.7344 - val_loss: 0.7979 - val_accuracy: 0.7188
Epoch 76/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7453 - accuracy: 0.7031
5/5 [==============================] - 0s 2ms/step - loss: 0.7693 - accuracy: 0.7359

5/5 [==============================] - 0s 46ms/step - loss: 0.7693 - accuracy: 0.7359 - val_loss: 0.7952 - val_accuracy: 0.7125
Epoch 77/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7939 - accuracy: 0.6953
5/5 [==============================] - 0s 2ms/step - loss: 0.7667 - accuracy: 0.7328

5/5 [==============================] - 0s 42ms/step - loss: 0.7667 - accuracy: 0.7328 - val_loss: 0.7925 - val_accuracy: 0.7250
Epoch 78/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8420 - accuracy: 0.7344
5/5 [==============================] - 0s 1ms/step - loss: 0.7639 - accuracy: 0.7344

5/5 [==============================] - 0s 43ms/step - loss: 0.7639 - accuracy: 0.7344 - val_loss: 0.7899 - val_accuracy: 0.7250
Epoch 79/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7514 - accuracy: 0.7734
5/5 [==============================] - 0s 1ms/step - loss: 0.7614 - accuracy: 0.7344

5/5 [==============================] - 0s 43ms/step - loss: 0.7614 - accuracy: 0.7344 - val_loss: 0.7876 - val_accuracy: 0.7188
Epoch 80/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7135 - accuracy: 0.7031
5/5 [==============================] - 0s 1ms/step - loss: 0.7588 - accuracy: 0.7344

5/5 [==============================] - 0s 43ms/step - loss: 0.7588 - accuracy: 0.7344 - val_loss: 0.7851 - val_accuracy: 0.7188
Epoch 81/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7290 - accuracy: 0.7109
5/5 [==============================] - 0s 2ms/step - loss: 0.7563 - accuracy: 0.7344

5/5 [==============================] - 0s 43ms/step - loss: 0.7563 - accuracy: 0.7344 - val_loss: 0.7826 - val_accuracy: 0.7188
Epoch 82/100

1/5 [=====>........................] - ETA: 0s - loss: 0.6891 - accuracy: 0.7500
5/5 [==============================] - 0s 1ms/step - loss: 0.7539 - accuracy: 0.7359

5/5 [==============================] - 0s 46ms/step - loss: 0.7539 - accuracy: 0.7359 - val_loss: 0.7801 - val_accuracy: 0.7188
Epoch 83/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7581 - accuracy: 0.7500
5/5 [==============================] - 0s 2ms/step - loss: 0.7516 - accuracy: 0.7375

5/5 [==============================] - 0s 43ms/step - loss: 0.7516 - accuracy: 0.7375 - val_loss: 0.7776 - val_accuracy: 0.7250
Epoch 84/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7194 - accuracy: 0.7656
5/5 [==============================] - 0s 1ms/step - loss: 0.7491 - accuracy: 0.7391

5/5 [==============================] - 0s 42ms/step - loss: 0.7491 - accuracy: 0.7391 - val_loss: 0.7752 - val_accuracy: 0.7250
Epoch 85/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7028 - accuracy: 0.7422
5/5 [==============================] - 0s 1ms/step - loss: 0.7468 - accuracy: 0.7406

5/5 [==============================] - 0s 46ms/step - loss: 0.7468 - accuracy: 0.7406 - val_loss: 0.7730 - val_accuracy: 0.7250
Epoch 86/100

1/5 [=====>........................] - ETA: 0s - loss: 0.6843 - accuracy: 0.7812
5/5 [==============================] - 0s 2ms/step - loss: 0.7446 - accuracy: 0.7422

5/5 [==============================] - 0s 46ms/step - loss: 0.7446 - accuracy: 0.7422 - val_loss: 0.7707 - val_accuracy: 0.7312
Epoch 87/100

1/5 [=====>........................] - ETA: 0s - loss: 0.8240 - accuracy: 0.7500
5/5 [==============================] - 0s 2ms/step - loss: 0.7423 - accuracy: 0.7437

5/5 [==============================] - 0s 46ms/step - loss: 0.7423 - accuracy: 0.7437 - val_loss: 0.7685 - val_accuracy: 0.7375
Epoch 88/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7387 - accuracy: 0.7031
5/5 [==============================] - 0s 1ms/step - loss: 0.7403 - accuracy: 0.7437

5/5 [==============================] - 0s 46ms/step - loss: 0.7403 - accuracy: 0.7437 - val_loss: 0.7664 - val_accuracy: 0.7312
Epoch 89/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7057 - accuracy: 0.7656
5/5 [==============================] - 0s 2ms/step - loss: 0.7381 - accuracy: 0.7437

5/5 [==============================] - 0s 43ms/step - loss: 0.7381 - accuracy: 0.7437 - val_loss: 0.7641 - val_accuracy: 0.7375
Epoch 90/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7002 - accuracy: 0.7422
5/5 [==============================] - 0s 1ms/step - loss: 0.7360 - accuracy: 0.7437

5/5 [==============================] - 0s 43ms/step - loss: 0.7360 - accuracy: 0.7437 - val_loss: 0.7621 - val_accuracy: 0.7312
Epoch 91/100

1/5 [=====>........................] - ETA: 0s - loss: 0.6020 - accuracy: 0.7969
5/5 [==============================] - 0s 1ms/step - loss: 0.7339 - accuracy: 0.7437

5/5 [==============================] - 0s 101ms/step - loss: 0.7339 - accuracy: 0.7437 - val_loss: 0.7600 - val_accuracy: 0.7312
Epoch 92/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7253 - accuracy: 0.7109
5/5 [==============================] - 0s 1ms/step - loss: 0.7319 - accuracy: 0.7437

5/5 [==============================] - 0s 46ms/step - loss: 0.7319 - accuracy: 0.7437 - val_loss: 0.7580 - val_accuracy: 0.7250
Epoch 93/100

1/5 [=====>........................] - ETA: 0s - loss: 0.6870 - accuracy: 0.7109
5/5 [==============================] - 0s 2ms/step - loss: 0.7299 - accuracy: 0.7453

5/5 [==============================] - 0s 42ms/step - loss: 0.7299 - accuracy: 0.7453 - val_loss: 0.7558 - val_accuracy: 0.7312
Epoch 94/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7093 - accuracy: 0.7812
5/5 [==============================] - 0s 1ms/step - loss: 0.7279 - accuracy: 0.7484

5/5 [==============================] - 0s 39ms/step - loss: 0.7279 - accuracy: 0.7484 - val_loss: 0.7537 - val_accuracy: 0.7312
Epoch 95/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7686 - accuracy: 0.6875
5/5 [==============================] - 0s 1ms/step - loss: 0.7259 - accuracy: 0.7500

5/5 [==============================] - 0s 42ms/step - loss: 0.7259 - accuracy: 0.7500 - val_loss: 0.7516 - val_accuracy: 0.7312
Epoch 96/100

1/5 [=====>........................] - ETA: 0s - loss: 0.6762 - accuracy: 0.7344
5/5 [==============================] - 0s 1ms/step - loss: 0.7240 - accuracy: 0.7500

5/5 [==============================] - 0s 39ms/step - loss: 0.7240 - accuracy: 0.7500 - val_loss: 0.7497 - val_accuracy: 0.7375
Epoch 97/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7657 - accuracy: 0.6953
5/5 [==============================] - 0s 2ms/step - loss: 0.7222 - accuracy: 0.7516

5/5 [==============================] - 0s 43ms/step - loss: 0.7222 - accuracy: 0.7516 - val_loss: 0.7477 - val_accuracy: 0.7312
Epoch 98/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7536 - accuracy: 0.7422
5/5 [==============================] - 0s 1ms/step - loss: 0.7203 - accuracy: 0.7516

5/5 [==============================] - 0s 41ms/step - loss: 0.7203 - accuracy: 0.7516 - val_loss: 0.7459 - val_accuracy: 0.7375
Epoch 99/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7067 - accuracy: 0.7812
5/5 [==============================] - 0s 1ms/step - loss: 0.7185 - accuracy: 0.7516

5/5 [==============================] - 0s 46ms/step - loss: 0.7185 - accuracy: 0.7516 - val_loss: 0.7441 - val_accuracy: 0.7312
Epoch 100/100

1/5 [=====>........................] - ETA: 0s - loss: 0.7655 - accuracy: 0.7188
5/5 [==============================] - 0s 1ms/step - loss: 0.7166 - accuracy: 0.7516

5/5 [==============================] - 0s 47ms/step - loss: 0.7166 - accuracy: 0.7516 - val_loss: 0.7423 - val_accuracy: 0.7312

end <- Sys.time()

runtime.128 <- round(as.numeric(difftime(time1 = end, time2 = begin, units = "secs")), 2)
  
pred.prob <- predict(model.128, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.128 <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# Batch 256
model.256 <- NULL
begin <- Sys.time()
model.256 <- create_model(256)

Epoch 1/100

1/3 [=========>....................] - ETA: 0s - loss: 1.5501 - accuracy: 0.2969
3/3 [==============================] - 0s 2ms/step - loss: 1.5587 - accuracy: 0.2828

3/3 [==============================] - 1s 331ms/step - loss: 1.5587 - accuracy: 0.2828 - val_loss: 1.4895 - val_accuracy: 0.3187
Epoch 2/100

1/3 [=========>....................] - ETA: 0s - loss: 1.5923 - accuracy: 0.3086
3/3 [==============================] - 0s 3ms/step - loss: 1.5455 - accuracy: 0.2953

3/3 [==============================] - 0s 76ms/step - loss: 1.5455 - accuracy: 0.2953 - val_loss: 1.4767 - val_accuracy: 0.3187
Epoch 3/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4930 - accuracy: 0.3359
3/3 [==============================] - 0s 1ms/step - loss: 1.5321 - accuracy: 0.2984

3/3 [==============================] - 0s 85ms/step - loss: 1.5321 - accuracy: 0.2984 - val_loss: 1.4644 - val_accuracy: 0.3187
Epoch 4/100

1/3 [=========>....................] - ETA: 0s - loss: 1.5344 - accuracy: 0.3281
3/3 [==============================] - 0s 2ms/step - loss: 1.5197 - accuracy: 0.3047

3/3 [==============================] - 0s 86ms/step - loss: 1.5197 - accuracy: 0.3047 - val_loss: 1.4523 - val_accuracy: 0.3187
Epoch 5/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4582 - accuracy: 0.3359
3/3 [==============================] - 0s 3ms/step - loss: 1.5070 - accuracy: 0.3047

3/3 [==============================] - 0s 90ms/step - loss: 1.5070 - accuracy: 0.3047 - val_loss: 1.4406 - val_accuracy: 0.3187
Epoch 6/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4317 - accuracy: 0.3359
3/3 [==============================] - 0s 1ms/step - loss: 1.4948 - accuracy: 0.3031

3/3 [==============================] - 0s 85ms/step - loss: 1.4948 - accuracy: 0.3031 - val_loss: 1.4290 - val_accuracy: 0.3250
Epoch 7/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4888 - accuracy: 0.3086
3/3 [==============================] - 0s 2ms/step - loss: 1.4834 - accuracy: 0.3016

3/3 [==============================] - 0s 94ms/step - loss: 1.4834 - accuracy: 0.3016 - val_loss: 1.4178 - val_accuracy: 0.3250
Epoch 8/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4805 - accuracy: 0.2852
3/3 [==============================] - 0s 2ms/step - loss: 1.4719 - accuracy: 0.3031

3/3 [==============================] - 0s 85ms/step - loss: 1.4719 - accuracy: 0.3031 - val_loss: 1.4069 - val_accuracy: 0.3187
Epoch 9/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4891 - accuracy: 0.3086
3/3 [==============================] - 0s 2ms/step - loss: 1.4607 - accuracy: 0.3063

3/3 [==============================] - 0s 86ms/step - loss: 1.4607 - accuracy: 0.3063 - val_loss: 1.3964 - val_accuracy: 0.3187
Epoch 10/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4842 - accuracy: 0.2969
3/3 [==============================] - 0s 2ms/step - loss: 1.4499 - accuracy: 0.3094

3/3 [==============================] - 0s 84ms/step - loss: 1.4499 - accuracy: 0.3094 - val_loss: 1.3862 - val_accuracy: 0.3187
Epoch 11/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4526 - accuracy: 0.3125
3/3 [==============================] - 0s 1ms/step - loss: 1.4390 - accuracy: 0.3109

3/3 [==============================] - 0s 86ms/step - loss: 1.4390 - accuracy: 0.3109 - val_loss: 1.3764 - val_accuracy: 0.3313
Epoch 12/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4181 - accuracy: 0.2969
3/3 [==============================] - 0s 2ms/step - loss: 1.4286 - accuracy: 0.3141

3/3 [==============================] - 0s 85ms/step - loss: 1.4286 - accuracy: 0.3141 - val_loss: 1.3669 - val_accuracy: 0.3313
Epoch 13/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4548 - accuracy: 0.3164
3/3 [==============================] - 0s 2ms/step - loss: 1.4190 - accuracy: 0.3172

3/3 [==============================] - 0s 86ms/step - loss: 1.4190 - accuracy: 0.3172 - val_loss: 1.3575 - val_accuracy: 0.3313
Epoch 14/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3471 - accuracy: 0.3477
3/3 [==============================] - 0s 2ms/step - loss: 1.4087 - accuracy: 0.3172

3/3 [==============================] - 0s 78ms/step - loss: 1.4087 - accuracy: 0.3172 - val_loss: 1.3486 - val_accuracy: 0.3375
Epoch 15/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3527 - accuracy: 0.3438
3/3 [==============================] - 0s 2ms/step - loss: 1.3993 - accuracy: 0.3203

3/3 [==============================] - 0s 92ms/step - loss: 1.3993 - accuracy: 0.3203 - val_loss: 1.3398 - val_accuracy: 0.3500
Epoch 16/100

1/3 [=========>....................] - ETA: 0s - loss: 1.4011 - accuracy: 0.3242
3/3 [==============================] - 0s 5ms/step - loss: 1.3902 - accuracy: 0.3281

3/3 [==============================] - 0s 92ms/step - loss: 1.3902 - accuracy: 0.3281 - val_loss: 1.3313 - val_accuracy: 0.3562
Epoch 17/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3538 - accuracy: 0.3203
3/3 [==============================] - 0s 2ms/step - loss: 1.3812 - accuracy: 0.3281

3/3 [==============================] - 0s 92ms/step - loss: 1.3812 - accuracy: 0.3281 - val_loss: 1.3231 - val_accuracy: 0.3750
Epoch 18/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3439 - accuracy: 0.3438
3/3 [==============================] - 0s 2ms/step - loss: 1.3723 - accuracy: 0.3281

3/3 [==============================] - 0s 90ms/step - loss: 1.3723 - accuracy: 0.3281 - val_loss: 1.3150 - val_accuracy: 0.3750
Epoch 19/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3844 - accuracy: 0.3086
3/3 [==============================] - 0s 3ms/step - loss: 1.3638 - accuracy: 0.3359

3/3 [==============================] - 0s 93ms/step - loss: 1.3638 - accuracy: 0.3359 - val_loss: 1.3072 - val_accuracy: 0.3812
Epoch 20/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3422 - accuracy: 0.3320
3/3 [==============================] - 0s 4ms/step - loss: 1.3553 - accuracy: 0.3453

3/3 [==============================] - 0s 94ms/step - loss: 1.3553 - accuracy: 0.3453 - val_loss: 1.2995 - val_accuracy: 0.3812
Epoch 21/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3384 - accuracy: 0.3281
3/3 [==============================] - 0s 4ms/step - loss: 1.3470 - accuracy: 0.3484

3/3 [==============================] - 0s 92ms/step - loss: 1.3470 - accuracy: 0.3484 - val_loss: 1.2920 - val_accuracy: 0.3812
Epoch 22/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3885 - accuracy: 0.3125
3/3 [==============================] - 0s 2ms/step - loss: 1.3391 - accuracy: 0.3453

3/3 [==============================] - 0s 101ms/step - loss: 1.3391 - accuracy: 0.3453 - val_loss: 1.2847 - val_accuracy: 0.3812
Epoch 23/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3321 - accuracy: 0.3789
3/3 [==============================] - 0s 1ms/step - loss: 1.3310 - accuracy: 0.3516

3/3 [==============================] - 0s 84ms/step - loss: 1.3310 - accuracy: 0.3516 - val_loss: 1.2776 - val_accuracy: 0.3875
Epoch 24/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3104 - accuracy: 0.3945
3/3 [==============================] - 0s 2ms/step - loss: 1.3234 - accuracy: 0.3578

3/3 [==============================] - 0s 93ms/step - loss: 1.3234 - accuracy: 0.3578 - val_loss: 1.2707 - val_accuracy: 0.3875
Epoch 25/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2992 - accuracy: 0.3750
3/3 [==============================] - 0s 2ms/step - loss: 1.3159 - accuracy: 0.3641

3/3 [==============================] - 0s 85ms/step - loss: 1.3159 - accuracy: 0.3641 - val_loss: 1.2639 - val_accuracy: 0.3875
Epoch 26/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3486 - accuracy: 0.3672
3/3 [==============================] - 0s 2ms/step - loss: 1.3085 - accuracy: 0.3656

3/3 [==============================] - 0s 86ms/step - loss: 1.3085 - accuracy: 0.3656 - val_loss: 1.2572 - val_accuracy: 0.3875
Epoch 27/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3462 - accuracy: 0.3086
3/3 [==============================] - 0s 3ms/step - loss: 1.3014 - accuracy: 0.3656

3/3 [==============================] - 0s 92ms/step - loss: 1.3014 - accuracy: 0.3656 - val_loss: 1.2507 - val_accuracy: 0.3875
Epoch 28/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2890 - accuracy: 0.4023
3/3 [==============================] - 0s 5ms/step - loss: 1.2938 - accuracy: 0.3656

3/3 [==============================] - 0s 83ms/step - loss: 1.2938 - accuracy: 0.3656 - val_loss: 1.2443 - val_accuracy: 0.3938
Epoch 29/100

1/3 [=========>....................] - ETA: 0s - loss: 1.3354 - accuracy: 0.3672
3/3 [==============================] - 0s 1ms/step - loss: 1.2870 - accuracy: 0.3703

3/3 [==============================] - 0s 77ms/step - loss: 1.2870 - accuracy: 0.3703 - val_loss: 1.2380 - val_accuracy: 0.3938
Epoch 30/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2653 - accuracy: 0.3398
3/3 [==============================] - 0s 2ms/step - loss: 1.2800 - accuracy: 0.3672

3/3 [==============================] - 0s 84ms/step - loss: 1.2800 - accuracy: 0.3672 - val_loss: 1.2318 - val_accuracy: 0.4000
Epoch 31/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2675 - accuracy: 0.3906
3/3 [==============================] - 0s 3ms/step - loss: 1.2730 - accuracy: 0.3656

3/3 [==============================] - 0s 86ms/step - loss: 1.2730 - accuracy: 0.3656 - val_loss: 1.2259 - val_accuracy: 0.4000
Epoch 32/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2559 - accuracy: 0.3711
3/3 [==============================] - 0s 2ms/step - loss: 1.2665 - accuracy: 0.3672

3/3 [==============================] - 0s 93ms/step - loss: 1.2665 - accuracy: 0.3672 - val_loss: 1.2200 - val_accuracy: 0.4062
Epoch 33/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2660 - accuracy: 0.3711
3/3 [==============================] - 0s 4ms/step - loss: 1.2598 - accuracy: 0.3672

3/3 [==============================] - 0s 83ms/step - loss: 1.2598 - accuracy: 0.3672 - val_loss: 1.2141 - val_accuracy: 0.4000
Epoch 34/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2919 - accuracy: 0.3633
3/3 [==============================] - 0s 2ms/step - loss: 1.2535 - accuracy: 0.3688

3/3 [==============================] - 0s 92ms/step - loss: 1.2535 - accuracy: 0.3688 - val_loss: 1.2084 - val_accuracy: 0.4000
Epoch 35/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2291 - accuracy: 0.3672
3/3 [==============================] - 0s 1ms/step - loss: 1.2469 - accuracy: 0.3688

3/3 [==============================] - 0s 85ms/step - loss: 1.2469 - accuracy: 0.3688 - val_loss: 1.2027 - val_accuracy: 0.4000
Epoch 36/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2422 - accuracy: 0.3750
3/3 [==============================] - 0s 2ms/step - loss: 1.2407 - accuracy: 0.3672

3/3 [==============================] - 0s 93ms/step - loss: 1.2407 - accuracy: 0.3672 - val_loss: 1.1972 - val_accuracy: 0.4125
Epoch 37/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2224 - accuracy: 0.3984
3/3 [==============================] - 0s 2ms/step - loss: 1.2343 - accuracy: 0.3672

3/3 [==============================] - 0s 101ms/step - loss: 1.2343 - accuracy: 0.3672 - val_loss: 1.1916 - val_accuracy: 0.4187
Epoch 38/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2057 - accuracy: 0.3945
3/3 [==============================] - 0s 2ms/step - loss: 1.2283 - accuracy: 0.3688

3/3 [==============================] - 0s 92ms/step - loss: 1.2283 - accuracy: 0.3688 - val_loss: 1.1862 - val_accuracy: 0.4187
Epoch 39/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2497 - accuracy: 0.3359
3/3 [==============================] - 0s 2ms/step - loss: 1.2224 - accuracy: 0.3703

3/3 [==============================] - 0s 77ms/step - loss: 1.2224 - accuracy: 0.3703 - val_loss: 1.1809 - val_accuracy: 0.4313
Epoch 40/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2472 - accuracy: 0.3672
3/3 [==============================] - 0s 4ms/step - loss: 1.2162 - accuracy: 0.3672

3/3 [==============================] - 0s 85ms/step - loss: 1.2162 - accuracy: 0.3672 - val_loss: 1.1757 - val_accuracy: 0.4313
Epoch 41/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2514 - accuracy: 0.3711
3/3 [==============================] - 0s 1ms/step - loss: 1.2104 - accuracy: 0.3641

3/3 [==============================] - 0s 93ms/step - loss: 1.2104 - accuracy: 0.3641 - val_loss: 1.1706 - val_accuracy: 0.4437
Epoch 42/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2022 - accuracy: 0.3672
3/3 [==============================] - 0s 2ms/step - loss: 1.2046 - accuracy: 0.3781

3/3 [==============================] - 0s 92ms/step - loss: 1.2046 - accuracy: 0.3781 - val_loss: 1.1655 - val_accuracy: 0.4437
Epoch 43/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2303 - accuracy: 0.3672
3/3 [==============================] - 0s 2ms/step - loss: 1.1987 - accuracy: 0.3812

3/3 [==============================] - 0s 86ms/step - loss: 1.1987 - accuracy: 0.3812 - val_loss: 1.1604 - val_accuracy: 0.4563
Epoch 44/100

1/3 [=========>....................] - ETA: 0s - loss: 1.2247 - accuracy: 0.3789
3/3 [==============================] - 0s 2ms/step - loss: 1.1932 - accuracy: 0.3969

3/3 [==============================] - 0s 85ms/step - loss: 1.1932 - accuracy: 0.3969 - val_loss: 1.1554 - val_accuracy: 0.4625
Epoch 45/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1769 - accuracy: 0.4336
3/3 [==============================] - 0s 2ms/step - loss: 1.1874 - accuracy: 0.4250

3/3 [==============================] - 0s 85ms/step - loss: 1.1874 - accuracy: 0.4250 - val_loss: 1.1504 - val_accuracy: 0.4625
Epoch 46/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1762 - accuracy: 0.4531
3/3 [==============================] - 0s 2ms/step - loss: 1.1818 - accuracy: 0.4703

3/3 [==============================] - 0s 85ms/step - loss: 1.1818 - accuracy: 0.4703 - val_loss: 1.1454 - val_accuracy: 0.5125
Epoch 47/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1675 - accuracy: 0.5234
3/3 [==============================] - 0s 3ms/step - loss: 1.1763 - accuracy: 0.5234

3/3 [==============================] - 0s 93ms/step - loss: 1.1763 - accuracy: 0.5234 - val_loss: 1.1405 - val_accuracy: 0.5688
Epoch 48/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1509 - accuracy: 0.5781
3/3 [==============================] - 0s 1ms/step - loss: 1.1709 - accuracy: 0.5578

3/3 [==============================] - 0s 78ms/step - loss: 1.1709 - accuracy: 0.5578 - val_loss: 1.1357 - val_accuracy: 0.6000
Epoch 49/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1624 - accuracy: 0.5977
3/3 [==============================] - 0s 3ms/step - loss: 1.1655 - accuracy: 0.5875

3/3 [==============================] - 0s 91ms/step - loss: 1.1655 - accuracy: 0.5875 - val_loss: 1.1309 - val_accuracy: 0.6000
Epoch 50/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1576 - accuracy: 0.6016
3/3 [==============================] - 0s 2ms/step - loss: 1.1600 - accuracy: 0.5859

3/3 [==============================] - 0s 84ms/step - loss: 1.1600 - accuracy: 0.5859 - val_loss: 1.1263 - val_accuracy: 0.6000
Epoch 51/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1481 - accuracy: 0.5977
3/3 [==============================] - 0s 3ms/step - loss: 1.1549 - accuracy: 0.5922

3/3 [==============================] - 0s 91ms/step - loss: 1.1549 - accuracy: 0.5922 - val_loss: 1.1217 - val_accuracy: 0.6000
Epoch 52/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1501 - accuracy: 0.6328
3/3 [==============================] - 0s 2ms/step - loss: 1.1494 - accuracy: 0.5969

3/3 [==============================] - 0s 93ms/step - loss: 1.1494 - accuracy: 0.5969 - val_loss: 1.1171 - val_accuracy: 0.5938
Epoch 53/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1317 - accuracy: 0.6211
3/3 [==============================] - 0s 3ms/step - loss: 1.1442 - accuracy: 0.6047

3/3 [==============================] - 0s 84ms/step - loss: 1.1442 - accuracy: 0.6047 - val_loss: 1.1126 - val_accuracy: 0.5813
Epoch 54/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1218 - accuracy: 0.6328
3/3 [==============================] - 0s 3ms/step - loss: 1.1391 - accuracy: 0.6109

3/3 [==============================] - 0s 93ms/step - loss: 1.1391 - accuracy: 0.6109 - val_loss: 1.1082 - val_accuracy: 0.5875
Epoch 55/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1370 - accuracy: 0.6133
3/3 [==============================] - 0s 2ms/step - loss: 1.1341 - accuracy: 0.6062

3/3 [==============================] - 0s 88ms/step - loss: 1.1341 - accuracy: 0.6062 - val_loss: 1.1038 - val_accuracy: 0.5813
Epoch 56/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1071 - accuracy: 0.6289
3/3 [==============================] - 0s 2ms/step - loss: 1.1290 - accuracy: 0.6141

3/3 [==============================] - 0s 92ms/step - loss: 1.1290 - accuracy: 0.6141 - val_loss: 1.0995 - val_accuracy: 0.5875
Epoch 57/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1165 - accuracy: 0.6211
3/3 [==============================] - 0s 1ms/step - loss: 1.1238 - accuracy: 0.6125

3/3 [==============================] - 0s 93ms/step - loss: 1.1238 - accuracy: 0.6125 - val_loss: 1.0951 - val_accuracy: 0.5875
Epoch 58/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1013 - accuracy: 0.6367
3/3 [==============================] - 0s 2ms/step - loss: 1.1188 - accuracy: 0.6187

3/3 [==============================] - 0s 93ms/step - loss: 1.1188 - accuracy: 0.6187 - val_loss: 1.0908 - val_accuracy: 0.5875
Epoch 59/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1115 - accuracy: 0.6055
3/3 [==============================] - 0s 2ms/step - loss: 1.1138 - accuracy: 0.6250

3/3 [==============================] - 0s 78ms/step - loss: 1.1138 - accuracy: 0.6250 - val_loss: 1.0864 - val_accuracy: 0.5938
Epoch 60/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1242 - accuracy: 0.6289
3/3 [==============================] - 0s 2ms/step - loss: 1.1088 - accuracy: 0.6266

3/3 [==============================] - 0s 93ms/step - loss: 1.1088 - accuracy: 0.6266 - val_loss: 1.0822 - val_accuracy: 0.6062
Epoch 61/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0921 - accuracy: 0.5977
3/3 [==============================] - 0s 2ms/step - loss: 1.1040 - accuracy: 0.6266

3/3 [==============================] - 0s 83ms/step - loss: 1.1040 - accuracy: 0.6266 - val_loss: 1.0779 - val_accuracy: 0.6062
Epoch 62/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0822 - accuracy: 0.6328
3/3 [==============================] - 0s 2ms/step - loss: 1.0990 - accuracy: 0.6391

3/3 [==============================] - 0s 74ms/step - loss: 1.0990 - accuracy: 0.6391 - val_loss: 1.0736 - val_accuracy: 0.6000
Epoch 63/100

1/3 [=========>....................] - ETA: 0s - loss: 1.1146 - accuracy: 0.6328
3/3 [==============================] - 0s 2ms/step - loss: 1.0941 - accuracy: 0.6422

3/3 [==============================] - 0s 85ms/step - loss: 1.0941 - accuracy: 0.6422 - val_loss: 1.0694 - val_accuracy: 0.6125
Epoch 64/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0597 - accuracy: 0.6602
3/3 [==============================] - 0s 2ms/step - loss: 1.0891 - accuracy: 0.6438

3/3 [==============================] - 0s 93ms/step - loss: 1.0891 - accuracy: 0.6438 - val_loss: 1.0652 - val_accuracy: 0.6187
Epoch 65/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0887 - accuracy: 0.6289
3/3 [==============================] - 0s 2ms/step - loss: 1.0844 - accuracy: 0.6422

3/3 [==============================] - 0s 85ms/step - loss: 1.0844 - accuracy: 0.6422 - val_loss: 1.0610 - val_accuracy: 0.6062
Epoch 66/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0825 - accuracy: 0.6328
3/3 [==============================] - 0s 3ms/step - loss: 1.0797 - accuracy: 0.6422

3/3 [==============================] - 0s 86ms/step - loss: 1.0797 - accuracy: 0.6422 - val_loss: 1.0569 - val_accuracy: 0.6125
Epoch 67/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0645 - accuracy: 0.6562
3/3 [==============================] - 0s 1ms/step - loss: 1.0748 - accuracy: 0.6469

3/3 [==============================] - 0s 93ms/step - loss: 1.0748 - accuracy: 0.6469 - val_loss: 1.0528 - val_accuracy: 0.6125
Epoch 68/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0813 - accuracy: 0.6367
3/3 [==============================] - 0s 3ms/step - loss: 1.0700 - accuracy: 0.6484

3/3 [==============================] - 0s 92ms/step - loss: 1.0700 - accuracy: 0.6484 - val_loss: 1.0487 - val_accuracy: 0.6125
Epoch 69/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0612 - accuracy: 0.6875
3/3 [==============================] - 0s 2ms/step - loss: 1.0653 - accuracy: 0.6531

3/3 [==============================] - 0s 90ms/step - loss: 1.0653 - accuracy: 0.6531 - val_loss: 1.0446 - val_accuracy: 0.6125
Epoch 70/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0693 - accuracy: 0.6484
3/3 [==============================] - 0s 4ms/step - loss: 1.0607 - accuracy: 0.6578

3/3 [==============================] - 0s 93ms/step - loss: 1.0607 - accuracy: 0.6578 - val_loss: 1.0406 - val_accuracy: 0.6125
Epoch 71/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0538 - accuracy: 0.6602
3/3 [==============================] - 0s 3ms/step - loss: 1.0560 - accuracy: 0.6578

3/3 [==============================] - 0s 92ms/step - loss: 1.0560 - accuracy: 0.6578 - val_loss: 1.0365 - val_accuracy: 0.6125
Epoch 72/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0588 - accuracy: 0.6758
3/3 [==============================] - 0s 1ms/step - loss: 1.0514 - accuracy: 0.6578

3/3 [==============================] - 0s 85ms/step - loss: 1.0514 - accuracy: 0.6578 - val_loss: 1.0326 - val_accuracy: 0.6125
Epoch 73/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0516 - accuracy: 0.6406
3/3 [==============================] - 0s 2ms/step - loss: 1.0468 - accuracy: 0.6594

3/3 [==============================] - 0s 93ms/step - loss: 1.0468 - accuracy: 0.6594 - val_loss: 1.0285 - val_accuracy: 0.6125
Epoch 74/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0132 - accuracy: 0.7070
3/3 [==============================] - 0s 1ms/step - loss: 1.0422 - accuracy: 0.6625

3/3 [==============================] - 0s 85ms/step - loss: 1.0422 - accuracy: 0.6625 - val_loss: 1.0245 - val_accuracy: 0.6187
Epoch 75/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0662 - accuracy: 0.6523
3/3 [==============================] - 0s 2ms/step - loss: 1.0377 - accuracy: 0.6672

3/3 [==============================] - 0s 85ms/step - loss: 1.0377 - accuracy: 0.6672 - val_loss: 1.0205 - val_accuracy: 0.6250
Epoch 76/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0501 - accuracy: 0.7031
3/3 [==============================] - 0s 2ms/step - loss: 1.0331 - accuracy: 0.6719

3/3 [==============================] - 0s 85ms/step - loss: 1.0331 - accuracy: 0.6719 - val_loss: 1.0165 - val_accuracy: 0.6250
Epoch 77/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0206 - accuracy: 0.6641
3/3 [==============================] - 0s 1ms/step - loss: 1.0287 - accuracy: 0.6750

3/3 [==============================] - 0s 93ms/step - loss: 1.0287 - accuracy: 0.6750 - val_loss: 1.0126 - val_accuracy: 0.6250
Epoch 78/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0245 - accuracy: 0.6914
3/3 [==============================] - 0s 3ms/step - loss: 1.0243 - accuracy: 0.6750

3/3 [==============================] - 0s 83ms/step - loss: 1.0243 - accuracy: 0.6750 - val_loss: 1.0086 - val_accuracy: 0.6250
Epoch 79/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9947 - accuracy: 0.6914
3/3 [==============================] - 0s 2ms/step - loss: 1.0199 - accuracy: 0.6734

3/3 [==============================] - 0s 78ms/step - loss: 1.0199 - accuracy: 0.6734 - val_loss: 1.0047 - val_accuracy: 0.6250
Epoch 80/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9966 - accuracy: 0.6484
3/3 [==============================] - 0s 2ms/step - loss: 1.0155 - accuracy: 0.6734

3/3 [==============================] - 0s 93ms/step - loss: 1.0155 - accuracy: 0.6734 - val_loss: 1.0008 - val_accuracy: 0.6375
Epoch 81/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0173 - accuracy: 0.6641
3/3 [==============================] - 0s 2ms/step - loss: 1.0111 - accuracy: 0.6781

3/3 [==============================] - 0s 85ms/step - loss: 1.0111 - accuracy: 0.6781 - val_loss: 0.9969 - val_accuracy: 0.6375
Epoch 82/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0415 - accuracy: 0.6602
3/3 [==============================] - 0s 2ms/step - loss: 1.0068 - accuracy: 0.6797

3/3 [==============================] - 0s 92ms/step - loss: 1.0068 - accuracy: 0.6797 - val_loss: 0.9931 - val_accuracy: 0.6375
Epoch 83/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0059 - accuracy: 0.6875
3/3 [==============================] - 0s 1ms/step - loss: 1.0025 - accuracy: 0.6797

3/3 [==============================] - 0s 101ms/step - loss: 1.0025 - accuracy: 0.6797 - val_loss: 0.9892 - val_accuracy: 0.6438
Epoch 84/100

1/3 [=========>....................] - ETA: 0s - loss: 1.0298 - accuracy: 0.6406
3/3 [==============================] - 0s 2ms/step - loss: 0.9982 - accuracy: 0.6797

3/3 [==============================] - 0s 94ms/step - loss: 0.9982 - accuracy: 0.6797 - val_loss: 0.9855 - val_accuracy: 0.6500
Epoch 85/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9965 - accuracy: 0.6602
3/3 [==============================] - 0s 3ms/step - loss: 0.9939 - accuracy: 0.6844

3/3 [==============================] - 0s 84ms/step - loss: 0.9939 - accuracy: 0.6844 - val_loss: 0.9818 - val_accuracy: 0.6500
Epoch 86/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9725 - accuracy: 0.7109
3/3 [==============================] - 0s 1ms/step - loss: 0.9898 - accuracy: 0.6828

3/3 [==============================] - 0s 93ms/step - loss: 0.9898 - accuracy: 0.6828 - val_loss: 0.9781 - val_accuracy: 0.6500
Epoch 87/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9848 - accuracy: 0.7031
3/3 [==============================] - 0s 2ms/step - loss: 0.9855 - accuracy: 0.6891

3/3 [==============================] - 0s 210ms/step - loss: 0.9855 - accuracy: 0.6891 - val_loss: 0.9744 - val_accuracy: 0.6500
Epoch 88/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9760 - accuracy: 0.6875
3/3 [==============================] - 0s 1ms/step - loss: 0.9815 - accuracy: 0.6891

3/3 [==============================] - 0s 78ms/step - loss: 0.9815 - accuracy: 0.6891 - val_loss: 0.9708 - val_accuracy: 0.6500
Epoch 89/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9824 - accuracy: 0.7188
3/3 [==============================] - 0s 2ms/step - loss: 0.9772 - accuracy: 0.6891

3/3 [==============================] - 0s 85ms/step - loss: 0.9772 - accuracy: 0.6891 - val_loss: 0.9671 - val_accuracy: 0.6438
Epoch 90/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9600 - accuracy: 0.7266
3/3 [==============================] - 0s 3ms/step - loss: 0.9732 - accuracy: 0.6891

3/3 [==============================] - 0s 86ms/step - loss: 0.9732 - accuracy: 0.6891 - val_loss: 0.9634 - val_accuracy: 0.6375
Epoch 91/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9918 - accuracy: 0.6719
3/3 [==============================] - 0s 2ms/step - loss: 0.9691 - accuracy: 0.6922

3/3 [==============================] - 0s 77ms/step - loss: 0.9691 - accuracy: 0.6922 - val_loss: 0.9597 - val_accuracy: 0.6375
Epoch 92/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9972 - accuracy: 0.6719
3/3 [==============================] - 0s 2ms/step - loss: 0.9651 - accuracy: 0.6938

3/3 [==============================] - 0s 85ms/step - loss: 0.9651 - accuracy: 0.6938 - val_loss: 0.9560 - val_accuracy: 0.6438
Epoch 93/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9480 - accuracy: 0.7305
3/3 [==============================] - 0s 2ms/step - loss: 0.9610 - accuracy: 0.6969

3/3 [==============================] - 0s 92ms/step - loss: 0.9610 - accuracy: 0.6969 - val_loss: 0.9524 - val_accuracy: 0.6438
Epoch 94/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9759 - accuracy: 0.7031
3/3 [==============================] - 0s 2ms/step - loss: 0.9569 - accuracy: 0.6984

3/3 [==============================] - 0s 85ms/step - loss: 0.9569 - accuracy: 0.6984 - val_loss: 0.9488 - val_accuracy: 0.6375
Epoch 95/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9702 - accuracy: 0.6914
3/3 [==============================] - 0s 1ms/step - loss: 0.9530 - accuracy: 0.7000

3/3 [==============================] - 0s 77ms/step - loss: 0.9530 - accuracy: 0.7000 - val_loss: 0.9453 - val_accuracy: 0.6438
Epoch 96/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9513 - accuracy: 0.7266
3/3 [==============================] - 0s 2ms/step - loss: 0.9490 - accuracy: 0.7016

3/3 [==============================] - 0s 85ms/step - loss: 0.9490 - accuracy: 0.7016 - val_loss: 0.9418 - val_accuracy: 0.6438
Epoch 97/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9188 - accuracy: 0.6953
3/3 [==============================] - 0s 981us/step - loss: 0.9452 - accuracy: 0.7016

3/3 [==============================] - 0s 85ms/step - loss: 0.9452 - accuracy: 0.7016 - val_loss: 0.9384 - val_accuracy: 0.6438
Epoch 98/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9269 - accuracy: 0.6992
3/3 [==============================] - 0s 2ms/step - loss: 0.9412 - accuracy: 0.7031

3/3 [==============================] - 0s 90ms/step - loss: 0.9412 - accuracy: 0.7031 - val_loss: 0.9350 - val_accuracy: 0.6375
Epoch 99/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9452 - accuracy: 0.7148
3/3 [==============================] - 0s 4ms/step - loss: 0.9373 - accuracy: 0.7047

3/3 [==============================] - 0s 84ms/step - loss: 0.9373 - accuracy: 0.7047 - val_loss: 0.9315 - val_accuracy: 0.6375
Epoch 100/100

1/3 [=========>....................] - ETA: 0s - loss: 0.9029 - accuracy: 0.7109
3/3 [==============================] - 0s 2ms/step - loss: 0.9337 - accuracy: 0.7063

3/3 [==============================] - 0s 86ms/step - loss: 0.9337 - accuracy: 0.7063 - val_loss: 0.9281 - val_accuracy: 0.6375

end <- Sys.time()

runtime.256 <- round(as.numeric(difftime(time1 = end, time2 = begin, units = "secs")), 2)
  
pred.prob <- predict(model.256, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.256 <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

  print(paste('Batch size : ', 1))

[1] "Batch size :  1"

  print(paste("Time taken to train neural network: ", runtime.1, " (seconds)"))

[1] "Time taken to train neural network:  114.52  (seconds)"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.1[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.8"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.1[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.1[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.1[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.96 , Specificity =  0.97 , Balanced Accuracy =  0.97"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.1[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.1[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.1[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.77 , Specificity =  0.88 , Balanced Accuracy =  0.83"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.1[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.1[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.1[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.9 , Specificity =  0.93 , Balanced Accuracy =  0.92"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.1[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.1[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.1[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.52 , Specificity =  0.95 , Balanced Accuracy =  0.73"

  print(paste('Batch size : ', 32))

[1] "Batch size :  32"

  print(paste("Time taken to train neural network: ", runtime.32, " (seconds)"))

[1] "Time taken to train neural network:  20.01  (seconds)"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.32[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.78"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.32[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.32[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.32[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.98 , Specificity =  0.95 , Balanced Accuracy =  0.97"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.32[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.32[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.32[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.77 , Specificity =  0.88 , Balanced Accuracy =  0.83"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.32[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.32[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.32[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.83 , Specificity =  0.94 , Balanced Accuracy =  0.89"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.32[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.32[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.32[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.48 , Specificity =  0.93 , Balanced Accuracy =  0.7"

  print(paste('Batch size : ', 64))

[1] "Batch size :  64"

  print(paste("Time taken to train neural network: ", runtime.64, " (seconds)"))

[1] "Time taken to train neural network:  19.27  (seconds)"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.64[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.75"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.64[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.64[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.64[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.98 , Specificity =  0.94 , Balanced Accuracy =  0.96"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.64[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.64[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.64[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.81 , Specificity =  0.86 , Balanced Accuracy =  0.84"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.64[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.64[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.64[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.79 , Specificity =  0.92 , Balanced Accuracy =  0.85"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.64[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.64[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.64[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.38 , Specificity =  0.95 , Balanced Accuracy =  0.66"

  print(paste('Batch size : ', 128))

[1] "Batch size :  128"

  print(paste("Time taken to train neural network: ", runtime.128, " (seconds)"))

[1] "Time taken to train neural network:  19.07  (seconds)"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.128[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.76"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.128[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.128[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.128[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.98 , Specificity =  0.91 , Balanced Accuracy =  0.95"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.128[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.128[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.128[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.77 , Specificity =  0.89 , Balanced Accuracy =  0.83"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.128[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.128[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.128[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.83 , Specificity =  0.93 , Balanced Accuracy =  0.88"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.128[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.128[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.128[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.4 , Specificity =  0.94 , Balanced Accuracy =  0.67"

  print(paste('Batch size : ', 256))

[1] "Batch size :  256"

  print(paste("Time taken to train neural network: ", runtime.256, " (seconds)"))

[1] "Time taken to train neural network:  19.12  (seconds)"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.256[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.71"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.256[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.256[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.256[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.98 , Specificity =  0.9 , Balanced Accuracy =  0.94"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.256[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.256[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.256[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.74 , Specificity =  0.86 , Balanced Accuracy =  0.8"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.256[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.256[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.256[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.69 , Specificity =  0.95 , Balanced Accuracy =  0.82"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.256[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.256[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.256[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.38 , Specificity =  0.9 , Balanced Accuracy =  0.64"

---

Part 2.1(c)

---

Part 2.1(c)(i)

---

The increasing number of batch sizes results in more training samples picked up in a batch, and hence lesser computing time will be taken to compute errors and average errors compared to a batch size of 1 where each sample is treated as batch for a constant central processing prowess.

---

Part 2.1(c)(ii)

---

As batch size increased, balanced accuracy decreased except for batch size 128. Balanced Accuracy of batch size 1 is the highest. The specificity, sensitivity and balanced accuracy of Class 0 in all batch sizes remain above 0.90. However, Sensitivity of Class 3 declined per increasing batch size. The specificity, Sensitivity and Balanced accuracy across batch sizes 32, 64 and 128 appear to be fluctuating across all 3 classes however their fluctuations are highlighted by their overall accuracies. The model with batch size 256 appear to have given the worst performance. Models with lower batch sizes appear to have performed better due to more examples being trained on them per iterations.

---

Part 2.1(d)

---

create_model.n <- function(batch.size, neurons) {
  model <- keras_model_sequential() %>%
    layer_dense(units = 8, activation="relu", input_shape=c(3)) %>%
    layer_dense(units = neurons, activation="relu") %>%
    layer_dense(units = 4, activation="softmax")

  model %>% compile(loss = "categorical_crossentropy",
          optimizer="adam",
          metrics=c("accuracy"))
  
  model %>% fit(
    data.matrix(x.train.df),
    y.train.df.ohe,
    epochs=100,
    batch_size=batch.size,
    validation_split=0.20)
  
  return(model)
}

# New Hidden Layer [number of neurons = 6] & Batch Size = 1
model.6n <- create_model.n(1, 6)

Epoch 1/100

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640/640 [==============================] - 2s 2ms/step - loss: 1.1555 - accuracy: 0.4891 - val_loss: 1.0322 - val_accuracy: 0.6187
Epoch 2/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.8859 - accuracy: 0.6547

640/640 [==============================] - 1s 2ms/step - loss: 0.8859 - accuracy: 0.6547 - val_loss: 0.8736 - val_accuracy: 0.6250
Epoch 3/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.7637 - accuracy: 0.6719 - val_loss: 0.7825 - val_accuracy: 0.6250
Epoch 4/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6947 - accuracy: 0.6734 - val_loss: 0.7179 - val_accuracy: 0.6250
Epoch 5/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6521 - accuracy: 0.6719 - val_loss: 0.6761 - val_accuracy: 0.6250
Epoch 6/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6236 - accuracy: 0.6828 - val_loss: 0.6575 - val_accuracy: 0.6812
Epoch 7/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6062 - accuracy: 0.7172 - val_loss: 0.6390 - val_accuracy: 0.7000
Epoch 8/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5911 - accuracy: 0.7188 - val_loss: 0.6187 - val_accuracy: 0.7000
Epoch 9/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5808 - accuracy: 0.7234 - val_loss: 0.6082 - val_accuracy: 0.7375
Epoch 10/100

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640/640 [==============================] - 2s 2ms/step - loss: 0.5746 - accuracy: 0.7484 - val_loss: 0.6022 - val_accuracy: 0.7250
Epoch 11/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5674 - accuracy: 0.7625

640/640 [==============================] - 1s 2ms/step - loss: 0.5674 - accuracy: 0.7625 - val_loss: 0.5988 - val_accuracy: 0.7312
Epoch 12/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5613 - accuracy: 0.7563

640/640 [==============================] - 1s 2ms/step - loss: 0.5613 - accuracy: 0.7563 - val_loss: 0.5892 - val_accuracy: 0.7312
Epoch 13/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5583 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5583 - accuracy: 0.7766 - val_loss: 0.5894 - val_accuracy: 0.7312
Epoch 14/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5519 - accuracy: 0.7734

640/640 [==============================] - 1s 2ms/step - loss: 0.5519 - accuracy: 0.7734 - val_loss: 0.5898 - val_accuracy: 0.7063
Epoch 15/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5492 - accuracy: 0.7656

640/640 [==============================] - 1s 2ms/step - loss: 0.5492 - accuracy: 0.7656 - val_loss: 0.5748 - val_accuracy: 0.7375
Epoch 16/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5430 - accuracy: 0.7734

640/640 [==============================] - 1s 2ms/step - loss: 0.5430 - accuracy: 0.7734 - val_loss: 0.5773 - val_accuracy: 0.7312
Epoch 17/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5380 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5380 - accuracy: 0.7875 - val_loss: 0.5768 - val_accuracy: 0.7250
Epoch 18/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5361 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5361 - accuracy: 0.7891 - val_loss: 0.5741 - val_accuracy: 0.7250
Epoch 19/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5329 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5329 - accuracy: 0.7844 - val_loss: 0.5715 - val_accuracy: 0.7250
Epoch 20/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5263 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5263 - accuracy: 0.7875 - val_loss: 0.5772 - val_accuracy: 0.7375
Epoch 21/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5233 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5233 - accuracy: 0.7875 - val_loss: 0.5686 - val_accuracy: 0.7125
Epoch 22/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5182 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5182 - accuracy: 0.7891 - val_loss: 0.5720 - val_accuracy: 0.7312
Epoch 23/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5185 - accuracy: 0.7812

640/640 [==============================] - 1s 2ms/step - loss: 0.5185 - accuracy: 0.7812 - val_loss: 0.5579 - val_accuracy: 0.7375
Epoch 24/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5150 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5150 - accuracy: 0.7891 - val_loss: 0.5540 - val_accuracy: 0.7250
Epoch 25/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5143 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5143 - accuracy: 0.7953 - val_loss: 0.5577 - val_accuracy: 0.7312
Epoch 26/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5121 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5121 - accuracy: 0.7859 - val_loss: 0.5588 - val_accuracy: 0.7375
Epoch 27/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5093 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5093 - accuracy: 0.7859 - val_loss: 0.5597 - val_accuracy: 0.7437
Epoch 28/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5082 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.5082 - accuracy: 0.8016 - val_loss: 0.5516 - val_accuracy: 0.7437
Epoch 29/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5069 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.5069 - accuracy: 0.7969 - val_loss: 0.5538 - val_accuracy: 0.7312
Epoch 30/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5029 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.5029 - accuracy: 0.7922 - val_loss: 0.5524 - val_accuracy: 0.7437
Epoch 31/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5053 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5053 - accuracy: 0.7953 - val_loss: 0.5450 - val_accuracy: 0.7375
Epoch 32/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5034 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5034 - accuracy: 0.7984 - val_loss: 0.5468 - val_accuracy: 0.7437
Epoch 33/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5001 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5001 - accuracy: 0.7953 - val_loss: 0.5574 - val_accuracy: 0.7500
Epoch 34/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5011 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5011 - accuracy: 0.7953 - val_loss: 0.5512 - val_accuracy: 0.7500
Epoch 35/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5017 - accuracy: 0.8000

640/640 [==============================] - 1s 2ms/step - loss: 0.5017 - accuracy: 0.8000 - val_loss: 0.5450 - val_accuracy: 0.7437
Epoch 36/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4980 - accuracy: 0.8000

640/640 [==============================] - 1s 2ms/step - loss: 0.4980 - accuracy: 0.8000 - val_loss: 0.5428 - val_accuracy: 0.7688
Epoch 37/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4983 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.4983 - accuracy: 0.7906 - val_loss: 0.5393 - val_accuracy: 0.7563
Epoch 38/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4998 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.4998 - accuracy: 0.7922 - val_loss: 0.5402 - val_accuracy: 0.7500
Epoch 39/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4935 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.4935 - accuracy: 0.7969 - val_loss: 0.5473 - val_accuracy: 0.7500
Epoch 40/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4944 - accuracy: 0.8031

640/640 [==============================] - 1s 2ms/step - loss: 0.4944 - accuracy: 0.8031 - val_loss: 0.5497 - val_accuracy: 0.7688
Epoch 41/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4948 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4948 - accuracy: 0.8016 - val_loss: 0.5463 - val_accuracy: 0.7563
Epoch 42/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4930 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4930 - accuracy: 0.8016 - val_loss: 0.5349 - val_accuracy: 0.7688
Epoch 43/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4918 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4918 - accuracy: 0.8016 - val_loss: 0.5353 - val_accuracy: 0.7437
Epoch 44/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4912 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.4912 - accuracy: 0.7969 - val_loss: 0.5424 - val_accuracy: 0.7625
Epoch 45/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4931 - accuracy: 0.8000

640/640 [==============================] - 1s 2ms/step - loss: 0.4931 - accuracy: 0.8000 - val_loss: 0.5286 - val_accuracy: 0.7812
Epoch 46/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4899 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4899 - accuracy: 0.8062 - val_loss: 0.5321 - val_accuracy: 0.7812
Epoch 47/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4895 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4895 - accuracy: 0.8078 - val_loss: 0.5354 - val_accuracy: 0.7750
Epoch 48/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4897 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4897 - accuracy: 0.7984 - val_loss: 0.5280 - val_accuracy: 0.7812
Epoch 49/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4892 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4892 - accuracy: 0.7984 - val_loss: 0.5290 - val_accuracy: 0.7750
Epoch 50/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4889 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4889 - accuracy: 0.8109 - val_loss: 0.5360 - val_accuracy: 0.7750
Epoch 51/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4867 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4867 - accuracy: 0.8062 - val_loss: 0.5462 - val_accuracy: 0.7625
Epoch 52/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4862 - accuracy: 0.8062 - val_loss: 0.5342 - val_accuracy: 0.7875
Epoch 53/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4871 - accuracy: 0.8078 - val_loss: 0.5315 - val_accuracy: 0.7750
Epoch 54/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4876 - accuracy: 0.8062 - val_loss: 0.5378 - val_accuracy: 0.7750
Epoch 55/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4844 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4844 - accuracy: 0.8078 - val_loss: 0.5357 - val_accuracy: 0.7750
Epoch 56/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4843 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4843 - accuracy: 0.8125 - val_loss: 0.5279 - val_accuracy: 0.7750
Epoch 57/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4842 - accuracy: 0.8062 - val_loss: 0.5307 - val_accuracy: 0.7688
Epoch 58/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4832 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4832 - accuracy: 0.8078 - val_loss: 0.5281 - val_accuracy: 0.7750
Epoch 59/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4835 - accuracy: 0.7969 - val_loss: 0.5311 - val_accuracy: 0.7750
Epoch 60/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4813 - accuracy: 0.8109 - val_loss: 0.5276 - val_accuracy: 0.8000
Epoch 61/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4805 - accuracy: 0.8078 - val_loss: 0.5387 - val_accuracy: 0.7688
Epoch 62/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4798 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4798 - accuracy: 0.8078 - val_loss: 0.5303 - val_accuracy: 0.7875
Epoch 63/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4832 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4832 - accuracy: 0.8062 - val_loss: 0.5255 - val_accuracy: 0.7750
Epoch 64/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4822 - accuracy: 0.8078 - val_loss: 0.5296 - val_accuracy: 0.7812
Epoch 65/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4792 - accuracy: 0.8156

640/640 [==============================] - 1s 2ms/step - loss: 0.4792 - accuracy: 0.8156 - val_loss: 0.5311 - val_accuracy: 0.7750
Epoch 66/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4788 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4788 - accuracy: 0.8016 - val_loss: 0.5309 - val_accuracy: 0.7750
Epoch 67/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4804 - accuracy: 0.8047 - val_loss: 0.5277 - val_accuracy: 0.7812
Epoch 68/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4794 - accuracy: 0.8047 - val_loss: 0.5235 - val_accuracy: 0.8000
Epoch 69/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4772 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4772 - accuracy: 0.8062 - val_loss: 0.5227 - val_accuracy: 0.7812
Epoch 70/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4756 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4756 - accuracy: 0.8125 - val_loss: 0.5220 - val_accuracy: 0.7937
Epoch 71/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4792 - accuracy: 0.8078 - val_loss: 0.5223 - val_accuracy: 0.7875
Epoch 72/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4768 - accuracy: 0.8062 - val_loss: 0.5265 - val_accuracy: 0.7750
Epoch 73/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4751 - accuracy: 0.8078 - val_loss: 0.5267 - val_accuracy: 0.8000
Epoch 74/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4762 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4762 - accuracy: 0.8078 - val_loss: 0.5246 - val_accuracy: 0.7875
Epoch 75/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4758 - accuracy: 0.8016 - val_loss: 0.5263 - val_accuracy: 0.7812
Epoch 76/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4769 - accuracy: 0.8094 - val_loss: 0.5264 - val_accuracy: 0.8000
Epoch 77/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4750 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4750 - accuracy: 0.8109 - val_loss: 0.5233 - val_accuracy: 0.7812
Epoch 78/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4732 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4732 - accuracy: 0.8094 - val_loss: 0.5313 - val_accuracy: 0.7812
Epoch 79/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4768 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4768 - accuracy: 0.8062 - val_loss: 0.5374 - val_accuracy: 0.7750
Epoch 80/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4745 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4745 - accuracy: 0.8125 - val_loss: 0.5182 - val_accuracy: 0.7937
Epoch 81/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4743 - accuracy: 0.8078 - val_loss: 0.5291 - val_accuracy: 0.7875
Epoch 82/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4725 - accuracy: 0.8109 - val_loss: 0.5264 - val_accuracy: 0.7875
Epoch 83/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4717 - accuracy: 0.8078 - val_loss: 0.5296 - val_accuracy: 0.7875
Epoch 84/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4753 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4753 - accuracy: 0.8109 - val_loss: 0.5245 - val_accuracy: 0.7937
Epoch 85/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4705 - accuracy: 0.8062 - val_loss: 0.5226 - val_accuracy: 0.8000
Epoch 86/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4702 - accuracy: 0.8000

640/640 [==============================] - 1s 2ms/step - loss: 0.4702 - accuracy: 0.8000 - val_loss: 0.5179 - val_accuracy: 0.7875
Epoch 87/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4730 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4730 - accuracy: 0.8094 - val_loss: 0.5332 - val_accuracy: 0.7937
Epoch 88/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4720 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4720 - accuracy: 0.8062 - val_loss: 0.5310 - val_accuracy: 0.7812
Epoch 89/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4737 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4737 - accuracy: 0.8078 - val_loss: 0.5227 - val_accuracy: 0.7875
Epoch 90/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4719 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4719 - accuracy: 0.8062 - val_loss: 0.5251 - val_accuracy: 0.8000
Epoch 91/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4714 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4714 - accuracy: 0.8094 - val_loss: 0.5210 - val_accuracy: 0.7937
Epoch 92/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4693 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.4693 - accuracy: 0.8047 - val_loss: 0.5219 - val_accuracy: 0.7875
Epoch 93/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4692 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4692 - accuracy: 0.8125 - val_loss: 0.5235 - val_accuracy: 0.7937
Epoch 94/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4680 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4680 - accuracy: 0.8094 - val_loss: 0.5187 - val_accuracy: 0.7875
Epoch 95/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4720 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4720 - accuracy: 0.8078 - val_loss: 0.5121 - val_accuracy: 0.8000
Epoch 96/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4667 - accuracy: 0.8156

640/640 [==============================] - 1s 2ms/step - loss: 0.4667 - accuracy: 0.8156 - val_loss: 0.5134 - val_accuracy: 0.8000
Epoch 97/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4700 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4700 - accuracy: 0.8062 - val_loss: 0.5239 - val_accuracy: 0.7937
Epoch 98/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4682 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4682 - accuracy: 0.8078 - val_loss: 0.5198 - val_accuracy: 0.7875
Epoch 99/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4737 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4737 - accuracy: 0.8062 - val_loss: 0.5165 - val_accuracy: 0.7937
Epoch 100/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4682 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4682 - accuracy: 0.8125 - val_loss: 0.5153 - val_accuracy: 0.7937

  
pred.prob <- predict(model.6n, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.6n <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# New Hidden Layer [number of neurons = 6] & Batch Size = 1
model.7n <- create_model.n(1, 7)

Epoch 1/100

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640/640 [==============================] - 1s 2ms/step - loss: 1.2337 - accuracy: 0.3922

640/640 [==============================] - 2s 3ms/step - loss: 1.2337 - accuracy: 0.3922 - val_loss: 1.1207 - val_accuracy: 0.4437
Epoch 2/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.9960 - accuracy: 0.6031

640/640 [==============================] - 1s 2ms/step - loss: 0.9960 - accuracy: 0.6031 - val_loss: 0.9715 - val_accuracy: 0.6562
Epoch 3/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.7933 - accuracy: 0.6750

640/640 [==============================] - 1s 2ms/step - loss: 0.7933 - accuracy: 0.6750 - val_loss: 0.7228 - val_accuracy: 0.7188
Epoch 4/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6600 - accuracy: 0.7344

640/640 [==============================] - 1s 2ms/step - loss: 0.6600 - accuracy: 0.7344 - val_loss: 0.6657 - val_accuracy: 0.7437
Epoch 5/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6173 - accuracy: 0.7453

640/640 [==============================] - 1s 2ms/step - loss: 0.6173 - accuracy: 0.7453 - val_loss: 0.6360 - val_accuracy: 0.7250
Epoch 6/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5966 - accuracy: 0.7750

640/640 [==============================] - 1s 2ms/step - loss: 0.5966 - accuracy: 0.7750 - val_loss: 0.6370 - val_accuracy: 0.7063
Epoch 7/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5826 - accuracy: 0.7672

640/640 [==============================] - 1s 2ms/step - loss: 0.5826 - accuracy: 0.7672 - val_loss: 0.6068 - val_accuracy: 0.7312
Epoch 8/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5781 - accuracy: 0.7703

640/640 [==============================] - 1s 2ms/step - loss: 0.5781 - accuracy: 0.7703 - val_loss: 0.6038 - val_accuracy: 0.7125
Epoch 9/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5689 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5689 - accuracy: 0.7844 - val_loss: 0.5919 - val_accuracy: 0.7375
Epoch 10/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5636 - accuracy: 0.7703

640/640 [==============================] - 1s 2ms/step - loss: 0.5636 - accuracy: 0.7703 - val_loss: 0.5879 - val_accuracy: 0.7250
Epoch 11/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5615 - accuracy: 0.7781

640/640 [==============================] - 1s 2ms/step - loss: 0.5615 - accuracy: 0.7781 - val_loss: 0.5780 - val_accuracy: 0.7500
Epoch 12/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5545 - accuracy: 0.7750

640/640 [==============================] - 1s 2ms/step - loss: 0.5545 - accuracy: 0.7750 - val_loss: 0.5709 - val_accuracy: 0.7312
Epoch 13/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5491 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5491 - accuracy: 0.7875 - val_loss: 0.5839 - val_accuracy: 0.7188
Epoch 14/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5483 - accuracy: 0.7734 - val_loss: 0.5638 - val_accuracy: 0.7563
Epoch 15/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5426 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5426 - accuracy: 0.7859 - val_loss: 0.5696 - val_accuracy: 0.7500
Epoch 16/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5390 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5390 - accuracy: 0.7875 - val_loss: 0.5579 - val_accuracy: 0.7437
Epoch 17/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5324 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5324 - accuracy: 0.7844 - val_loss: 0.5835 - val_accuracy: 0.7000
Epoch 18/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5353 - accuracy: 0.7797

640/640 [==============================] - 1s 2ms/step - loss: 0.5353 - accuracy: 0.7797 - val_loss: 0.5605 - val_accuracy: 0.7563
Epoch 19/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5300 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.5300 - accuracy: 0.7984 - val_loss: 0.5626 - val_accuracy: 0.7625
Epoch 20/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5261 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5261 - accuracy: 0.7875 - val_loss: 0.5558 - val_accuracy: 0.7563
Epoch 21/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5269 - accuracy: 0.7844 - val_loss: 0.5488 - val_accuracy: 0.7563
Epoch 22/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5198 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5198 - accuracy: 0.7875 - val_loss: 0.5514 - val_accuracy: 0.7375
Epoch 23/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5230 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.5230 - accuracy: 0.7922 - val_loss: 0.5420 - val_accuracy: 0.7375
Epoch 24/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5165 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5165 - accuracy: 0.7891 - val_loss: 0.5345 - val_accuracy: 0.7437
Epoch 25/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5146 - accuracy: 0.7859

640/640 [==============================] - 1s 2ms/step - loss: 0.5146 - accuracy: 0.7859 - val_loss: 0.5356 - val_accuracy: 0.7625
Epoch 26/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5099 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5099 - accuracy: 0.7953 - val_loss: 0.5359 - val_accuracy: 0.7625
Epoch 27/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5084 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.5084 - accuracy: 0.7906 - val_loss: 0.5397 - val_accuracy: 0.7625
Epoch 28/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5036 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.5036 - accuracy: 0.8047 - val_loss: 0.5540 - val_accuracy: 0.7375
Epoch 29/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5046 - accuracy: 0.7937 - val_loss: 0.5306 - val_accuracy: 0.7625
Epoch 30/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5002 - accuracy: 0.7922 - val_loss: 0.5346 - val_accuracy: 0.7437
Epoch 31/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4998 - accuracy: 0.7937 - val_loss: 0.5302 - val_accuracy: 0.7437
Epoch 32/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4958 - accuracy: 0.8000 - val_loss: 0.5330 - val_accuracy: 0.7500
Epoch 33/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4940 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.4940 - accuracy: 0.7906 - val_loss: 0.5365 - val_accuracy: 0.7437
Epoch 34/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4925 - accuracy: 0.7953 - val_loss: 0.5297 - val_accuracy: 0.7625
Epoch 35/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4882 - accuracy: 0.8016 - val_loss: 0.5243 - val_accuracy: 0.7563
Epoch 36/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4887 - accuracy: 0.7953 - val_loss: 0.5228 - val_accuracy: 0.7688
Epoch 37/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4831 - accuracy: 0.8000 - val_loss: 0.5174 - val_accuracy: 0.7750
Epoch 38/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4832 - accuracy: 0.8062 - val_loss: 0.5244 - val_accuracy: 0.7688
Epoch 39/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4802 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4802 - accuracy: 0.8203 - val_loss: 0.5237 - val_accuracy: 0.7625
Epoch 40/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4797 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.4797 - accuracy: 0.8047 - val_loss: 0.5122 - val_accuracy: 0.7688
Epoch 41/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4774 - accuracy: 0.8141

640/640 [==============================] - 1s 2ms/step - loss: 0.4774 - accuracy: 0.8141 - val_loss: 0.5178 - val_accuracy: 0.7688
Epoch 42/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4766 - accuracy: 0.8172 - val_loss: 0.5212 - val_accuracy: 0.7625
Epoch 43/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4729 - accuracy: 0.8156

640/640 [==============================] - 1s 2ms/step - loss: 0.4729 - accuracy: 0.8156 - val_loss: 0.5231 - val_accuracy: 0.7688
Epoch 44/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4734 - accuracy: 0.8125 - val_loss: 0.5103 - val_accuracy: 0.7688
Epoch 45/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4704 - accuracy: 0.8078 - val_loss: 0.5135 - val_accuracy: 0.7563
Epoch 46/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4694 - accuracy: 0.8219 - val_loss: 0.5118 - val_accuracy: 0.7688
Epoch 47/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4666 - accuracy: 0.8203 - val_loss: 0.5114 - val_accuracy: 0.7688
Epoch 48/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4685 - accuracy: 0.8203 - val_loss: 0.5098 - val_accuracy: 0.7563
Epoch 49/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4644 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4644 - accuracy: 0.8234 - val_loss: 0.5158 - val_accuracy: 0.7625
Epoch 50/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4653 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4653 - accuracy: 0.8219 - val_loss: 0.5120 - val_accuracy: 0.7625
Epoch 51/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4638 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4638 - accuracy: 0.8188 - val_loss: 0.5206 - val_accuracy: 0.7625
Epoch 52/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4618 - accuracy: 0.8156

640/640 [==============================] - 1s 2ms/step - loss: 0.4618 - accuracy: 0.8156 - val_loss: 0.5066 - val_accuracy: 0.7688
Epoch 53/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4596 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4596 - accuracy: 0.8281 - val_loss: 0.5213 - val_accuracy: 0.7812
Epoch 54/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4621 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4621 - accuracy: 0.8281 - val_loss: 0.5195 - val_accuracy: 0.7625
Epoch 55/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4613 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4613 - accuracy: 0.8266 - val_loss: 0.5143 - val_accuracy: 0.7625
Epoch 56/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4619 - accuracy: 0.8219 - val_loss: 0.5145 - val_accuracy: 0.7812
Epoch 57/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4592 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4592 - accuracy: 0.8234 - val_loss: 0.5032 - val_accuracy: 0.7750
Epoch 58/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4592 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4592 - accuracy: 0.8219 - val_loss: 0.5145 - val_accuracy: 0.7563
Epoch 59/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4560 - accuracy: 0.8250

640/640 [==============================] - 1s 2ms/step - loss: 0.4560 - accuracy: 0.8250 - val_loss: 0.5148 - val_accuracy: 0.7812
Epoch 60/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4610 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4610 - accuracy: 0.8219 - val_loss: 0.5043 - val_accuracy: 0.7812
Epoch 61/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4555 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4555 - accuracy: 0.8313 - val_loss: 0.5188 - val_accuracy: 0.7688
Epoch 62/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4562 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4562 - accuracy: 0.8266 - val_loss: 0.5107 - val_accuracy: 0.7688
Epoch 63/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4561 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4561 - accuracy: 0.8219 - val_loss: 0.5128 - val_accuracy: 0.7750
Epoch 64/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4537 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4537 - accuracy: 0.8313 - val_loss: 0.5104 - val_accuracy: 0.7625
Epoch 65/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4551 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4551 - accuracy: 0.8281 - val_loss: 0.5062 - val_accuracy: 0.7812
Epoch 66/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4534 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4534 - accuracy: 0.8281 - val_loss: 0.5005 - val_accuracy: 0.7875
Epoch 67/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4511 - accuracy: 0.8328

640/640 [==============================] - 1s 2ms/step - loss: 0.4511 - accuracy: 0.8328 - val_loss: 0.5200 - val_accuracy: 0.7625
Epoch 68/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4542 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4542 - accuracy: 0.8219 - val_loss: 0.5079 - val_accuracy: 0.7875
Epoch 69/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4480 - accuracy: 0.8281 - val_loss: 0.5258 - val_accuracy: 0.7812
Epoch 70/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4532 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4532 - accuracy: 0.8313 - val_loss: 0.5286 - val_accuracy: 0.7688
Epoch 71/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4497 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4497 - accuracy: 0.8266 - val_loss: 0.5058 - val_accuracy: 0.7937
Epoch 72/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4461 - accuracy: 0.8359

640/640 [==============================] - 1s 2ms/step - loss: 0.4461 - accuracy: 0.8359 - val_loss: 0.5082 - val_accuracy: 0.7625
Epoch 73/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4482 - accuracy: 0.8328

640/640 [==============================] - 1s 2ms/step - loss: 0.4482 - accuracy: 0.8328 - val_loss: 0.5065 - val_accuracy: 0.7937
Epoch 74/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4496 - accuracy: 0.8391

640/640 [==============================] - 1s 2ms/step - loss: 0.4496 - accuracy: 0.8391 - val_loss: 0.5069 - val_accuracy: 0.7812
Epoch 75/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4474 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4474 - accuracy: 0.8281 - val_loss: 0.5165 - val_accuracy: 0.7875
Epoch 76/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4490 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4490 - accuracy: 0.8313 - val_loss: 0.5008 - val_accuracy: 0.7875
Epoch 77/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4447 - accuracy: 0.8375

640/640 [==============================] - 1s 2ms/step - loss: 0.4447 - accuracy: 0.8375 - val_loss: 0.5082 - val_accuracy: 0.7688
Epoch 78/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4455 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4455 - accuracy: 0.8281 - val_loss: 0.5072 - val_accuracy: 0.7812
Epoch 79/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4449 - accuracy: 0.8328

640/640 [==============================] - 1s 2ms/step - loss: 0.4449 - accuracy: 0.8328 - val_loss: 0.5051 - val_accuracy: 0.7688
Epoch 80/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4421 - accuracy: 0.8375

640/640 [==============================] - 1s 2ms/step - loss: 0.4421 - accuracy: 0.8375 - val_loss: 0.5160 - val_accuracy: 0.7812
Epoch 81/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4384 - accuracy: 0.8328

640/640 [==============================] - 1s 2ms/step - loss: 0.4384 - accuracy: 0.8328 - val_loss: 0.5166 - val_accuracy: 0.7875
Epoch 82/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4430 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4430 - accuracy: 0.8266 - val_loss: 0.4993 - val_accuracy: 0.8000
Epoch 83/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4398 - accuracy: 0.8328

640/640 [==============================] - 1s 2ms/step - loss: 0.4398 - accuracy: 0.8328 - val_loss: 0.5134 - val_accuracy: 0.7812
Epoch 84/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4421 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4421 - accuracy: 0.8344 - val_loss: 0.5064 - val_accuracy: 0.7812
Epoch 85/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4392 - accuracy: 0.8297

640/640 [==============================] - 1s 2ms/step - loss: 0.4392 - accuracy: 0.8297 - val_loss: 0.5004 - val_accuracy: 0.8250
Epoch 86/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4398 - accuracy: 0.8297

640/640 [==============================] - 1s 2ms/step - loss: 0.4398 - accuracy: 0.8297 - val_loss: 0.5003 - val_accuracy: 0.8000
Epoch 87/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4359 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4359 - accuracy: 0.8344 - val_loss: 0.5184 - val_accuracy: 0.7688
Epoch 88/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4402 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4402 - accuracy: 0.8344 - val_loss: 0.5011 - val_accuracy: 0.8000
Epoch 89/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4358 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4358 - accuracy: 0.8313 - val_loss: 0.4937 - val_accuracy: 0.8188
Epoch 90/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4372 - accuracy: 0.8250

640/640 [==============================] - 1s 2ms/step - loss: 0.4372 - accuracy: 0.8250 - val_loss: 0.4999 - val_accuracy: 0.8125
Epoch 91/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4370 - accuracy: 0.8359

640/640 [==============================] - 1s 2ms/step - loss: 0.4370 - accuracy: 0.8359 - val_loss: 0.5084 - val_accuracy: 0.7937
Epoch 92/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4360 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4360 - accuracy: 0.8344 - val_loss: 0.5083 - val_accuracy: 0.7812
Epoch 93/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4330 - accuracy: 0.8438

640/640 [==============================] - 1s 2ms/step - loss: 0.4330 - accuracy: 0.8438 - val_loss: 0.5038 - val_accuracy: 0.8000
Epoch 94/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4339 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4339 - accuracy: 0.8344 - val_loss: 0.5030 - val_accuracy: 0.8062
Epoch 95/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4333 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4333 - accuracy: 0.8281 - val_loss: 0.5136 - val_accuracy: 0.8000
Epoch 96/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4312 - accuracy: 0.8438

640/640 [==============================] - 1s 2ms/step - loss: 0.4312 - accuracy: 0.8438 - val_loss: 0.5077 - val_accuracy: 0.8062
Epoch 97/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4349 - accuracy: 0.8391 - val_loss: 0.5018 - val_accuracy: 0.7875
Epoch 98/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4322 - accuracy: 0.8328

640/640 [==============================] - 1s 2ms/step - loss: 0.4322 - accuracy: 0.8328 - val_loss: 0.5052 - val_accuracy: 0.8250
Epoch 99/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4304 - accuracy: 0.8344 - val_loss: 0.5003 - val_accuracy: 0.7937
Epoch 100/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4301 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4301 - accuracy: 0.8344 - val_loss: 0.5114 - val_accuracy: 0.8062

  
pred.prob <- predict(model.7n, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.7n <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# New Hidden Layer [number of neurons = 7] & Batch Size = 1
model.8n <- create_model.n(1, 8)

Epoch 1/100

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640/640 [==============================] - 1s 2ms/step - loss: 1.2391 - accuracy: 0.5031

640/640 [==============================] - 2s 3ms/step - loss: 1.2391 - accuracy: 0.5031 - val_loss: 1.0558 - val_accuracy: 0.5250
Epoch 2/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.9759 - accuracy: 0.5844

640/640 [==============================] - 1s 2ms/step - loss: 0.9759 - accuracy: 0.5844 - val_loss: 0.9278 - val_accuracy: 0.5875
Epoch 3/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.8660 - accuracy: 0.5984 - val_loss: 0.8490 - val_accuracy: 0.6187
Epoch 4/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.7820 - accuracy: 0.6516 - val_loss: 0.7416 - val_accuracy: 0.6250
Epoch 5/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6829 - accuracy: 0.6891

640/640 [==============================] - 1s 2ms/step - loss: 0.6829 - accuracy: 0.6891 - val_loss: 0.6743 - val_accuracy: 0.6375
Epoch 6/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6388 - accuracy: 0.7047 - val_loss: 0.6376 - val_accuracy: 0.7375
Epoch 7/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6164 - accuracy: 0.7344 - val_loss: 0.6163 - val_accuracy: 0.7312
Epoch 8/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5997 - accuracy: 0.7484

640/640 [==============================] - 1s 2ms/step - loss: 0.5997 - accuracy: 0.7484 - val_loss: 0.6138 - val_accuracy: 0.7188
Epoch 9/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5869 - accuracy: 0.7375

640/640 [==============================] - 1s 2ms/step - loss: 0.5869 - accuracy: 0.7375 - val_loss: 0.6101 - val_accuracy: 0.7125
Epoch 10/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5775 - accuracy: 0.7437

640/640 [==============================] - 1s 2ms/step - loss: 0.5775 - accuracy: 0.7437 - val_loss: 0.5921 - val_accuracy: 0.7500
Epoch 11/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5717 - accuracy: 0.7641 - val_loss: 0.5908 - val_accuracy: 0.7250
Epoch 12/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5644 - accuracy: 0.7516 - val_loss: 0.5839 - val_accuracy: 0.7250
Epoch 13/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5557 - accuracy: 0.7578

640/640 [==============================] - 1s 2ms/step - loss: 0.5557 - accuracy: 0.7578 - val_loss: 0.5734 - val_accuracy: 0.7625
Epoch 14/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5517 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5517 - accuracy: 0.7828 - val_loss: 0.5772 - val_accuracy: 0.7563
Epoch 15/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5457 - accuracy: 0.7719 - val_loss: 0.5683 - val_accuracy: 0.7375
Epoch 16/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5411 - accuracy: 0.7672 - val_loss: 0.5663 - val_accuracy: 0.7375
Epoch 17/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5365 - accuracy: 0.7797

640/640 [==============================] - 1s 2ms/step - loss: 0.5365 - accuracy: 0.7797 - val_loss: 0.5593 - val_accuracy: 0.7563
Epoch 18/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5282 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5282 - accuracy: 0.7766 - val_loss: 0.5558 - val_accuracy: 0.7688
Epoch 19/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5264 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5264 - accuracy: 0.7766 - val_loss: 0.5550 - val_accuracy: 0.7563
Epoch 20/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5212 - accuracy: 0.7688

640/640 [==============================] - 1s 2ms/step - loss: 0.5212 - accuracy: 0.7688 - val_loss: 0.5510 - val_accuracy: 0.7563
Epoch 21/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5157 - accuracy: 0.7875

640/640 [==============================] - 1s 2ms/step - loss: 0.5157 - accuracy: 0.7875 - val_loss: 0.5514 - val_accuracy: 0.7563
Epoch 22/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5137 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5137 - accuracy: 0.7828 - val_loss: 0.5470 - val_accuracy: 0.7688
Epoch 23/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5102 - accuracy: 0.7844

640/640 [==============================] - 1s 2ms/step - loss: 0.5102 - accuracy: 0.7844 - val_loss: 0.5432 - val_accuracy: 0.7437
Epoch 24/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5066 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5066 - accuracy: 0.7891 - val_loss: 0.5420 - val_accuracy: 0.7688
Epoch 25/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5038 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5038 - accuracy: 0.7891 - val_loss: 0.5362 - val_accuracy: 0.7563
Epoch 26/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5004 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.5004 - accuracy: 0.7937 - val_loss: 0.5450 - val_accuracy: 0.7563
Epoch 27/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4955 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.4955 - accuracy: 0.7891 - val_loss: 0.5446 - val_accuracy: 0.7625
Epoch 28/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4958 - accuracy: 0.7969

640/640 [==============================] - 1s 2ms/step - loss: 0.4958 - accuracy: 0.7969 - val_loss: 0.5348 - val_accuracy: 0.7688
Epoch 29/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4917 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.4917 - accuracy: 0.7953 - val_loss: 0.5369 - val_accuracy: 0.7625
Epoch 30/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4886 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.4886 - accuracy: 0.7953 - val_loss: 0.5417 - val_accuracy: 0.7563
Epoch 31/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4885 - accuracy: 0.8000 - val_loss: 0.5331 - val_accuracy: 0.7563
Epoch 32/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4860 - accuracy: 0.8016

640/640 [==============================] - 1s 2ms/step - loss: 0.4860 - accuracy: 0.8016 - val_loss: 0.5359 - val_accuracy: 0.7625
Epoch 33/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4841 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4841 - accuracy: 0.8062 - val_loss: 0.5374 - val_accuracy: 0.7688
Epoch 34/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4816 - accuracy: 0.8047 - val_loss: 0.5303 - val_accuracy: 0.7500
Epoch 35/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4802 - accuracy: 0.8078 - val_loss: 0.5390 - val_accuracy: 0.7437
Epoch 36/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4788 - accuracy: 0.7984

640/640 [==============================] - 1s 2ms/step - loss: 0.4788 - accuracy: 0.7984 - val_loss: 0.5383 - val_accuracy: 0.7625
Epoch 37/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4769 - accuracy: 0.8031 - val_loss: 0.5307 - val_accuracy: 0.7625
Epoch 38/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4749 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4749 - accuracy: 0.8078 - val_loss: 0.5387 - val_accuracy: 0.7437
Epoch 39/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4754 - accuracy: 0.8094 - val_loss: 0.5325 - val_accuracy: 0.7688
Epoch 40/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4751 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4751 - accuracy: 0.8109 - val_loss: 0.5323 - val_accuracy: 0.7625
Epoch 41/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4729 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4729 - accuracy: 0.8094 - val_loss: 0.5308 - val_accuracy: 0.7563
Epoch 42/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4716 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.4716 - accuracy: 0.7922 - val_loss: 0.5316 - val_accuracy: 0.7688
Epoch 43/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4702 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4702 - accuracy: 0.8109 - val_loss: 0.5320 - val_accuracy: 0.7563
Epoch 44/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4706 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4706 - accuracy: 0.8078 - val_loss: 0.5341 - val_accuracy: 0.7437
Epoch 45/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4696 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4696 - accuracy: 0.8062 - val_loss: 0.5323 - val_accuracy: 0.7500
Epoch 46/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4702 - accuracy: 0.8047

640/640 [==============================] - 1s 2ms/step - loss: 0.4702 - accuracy: 0.8047 - val_loss: 0.5309 - val_accuracy: 0.7625
Epoch 47/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4668 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4668 - accuracy: 0.8094 - val_loss: 0.5276 - val_accuracy: 0.7688
Epoch 48/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4665 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4665 - accuracy: 0.8172 - val_loss: 0.5314 - val_accuracy: 0.7625
Epoch 49/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4644 - accuracy: 0.8125 - val_loss: 0.5295 - val_accuracy: 0.7688
Epoch 50/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4664 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4664 - accuracy: 0.8219 - val_loss: 0.5287 - val_accuracy: 0.7625
Epoch 51/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4645 - accuracy: 0.8156

640/640 [==============================] - 2s 2ms/step - loss: 0.4645 - accuracy: 0.8156 - val_loss: 0.5259 - val_accuracy: 0.7688
Epoch 52/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4649 - accuracy: 0.8062

640/640 [==============================] - 2s 2ms/step - loss: 0.4649 - accuracy: 0.8062 - val_loss: 0.5300 - val_accuracy: 0.7500
Epoch 53/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4626 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4626 - accuracy: 0.8188 - val_loss: 0.5318 - val_accuracy: 0.7500
Epoch 54/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4638 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4638 - accuracy: 0.8172 - val_loss: 0.5320 - val_accuracy: 0.7563
Epoch 55/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4617 - accuracy: 0.8219

640/640 [==============================] - 2s 2ms/step - loss: 0.4617 - accuracy: 0.8219 - val_loss: 0.5414 - val_accuracy: 0.7688
Epoch 56/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4635 - accuracy: 0.8188

640/640 [==============================] - 2s 3ms/step - loss: 0.4635 - accuracy: 0.8188 - val_loss: 0.5365 - val_accuracy: 0.7375
Epoch 57/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4621 - accuracy: 0.8250

640/640 [==============================] - 2s 3ms/step - loss: 0.4621 - accuracy: 0.8250 - val_loss: 0.5275 - val_accuracy: 0.7688
Epoch 58/100

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640/640 [==============================] - 2s 3ms/step - loss: 0.4558 - accuracy: 0.8281 - val_loss: 0.5458 - val_accuracy: 0.7688
Epoch 59/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4605 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4605 - accuracy: 0.8219 - val_loss: 0.5346 - val_accuracy: 0.7625
Epoch 60/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4611 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4611 - accuracy: 0.8234 - val_loss: 0.5345 - val_accuracy: 0.7437
Epoch 61/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4588 - accuracy: 0.8172 - val_loss: 0.5328 - val_accuracy: 0.7625
Epoch 62/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4591 - accuracy: 0.8219 - val_loss: 0.5331 - val_accuracy: 0.7500
Epoch 63/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4550 - accuracy: 0.8125 - val_loss: 0.5417 - val_accuracy: 0.7437
Epoch 64/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4589 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4589 - accuracy: 0.8219 - val_loss: 0.5362 - val_accuracy: 0.7375
Epoch 65/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4585 - accuracy: 0.8188 - val_loss: 0.5339 - val_accuracy: 0.7437
Epoch 66/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4556 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4556 - accuracy: 0.8234 - val_loss: 0.5331 - val_accuracy: 0.7625
Epoch 67/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4572 - accuracy: 0.8297 - val_loss: 0.5251 - val_accuracy: 0.7625
Epoch 68/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4561 - accuracy: 0.8188 - val_loss: 0.5259 - val_accuracy: 0.7500
Epoch 69/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4576 - accuracy: 0.8250 - val_loss: 0.5326 - val_accuracy: 0.7375
Epoch 70/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4558 - accuracy: 0.8219 - val_loss: 0.5311 - val_accuracy: 0.7563
Epoch 71/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4531 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4531 - accuracy: 0.8188 - val_loss: 0.5440 - val_accuracy: 0.7437
Epoch 72/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4560 - accuracy: 0.8281 - val_loss: 0.5289 - val_accuracy: 0.7625
Epoch 73/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4567 - accuracy: 0.8172 - val_loss: 0.5319 - val_accuracy: 0.7563
Epoch 74/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4546 - accuracy: 0.8219 - val_loss: 0.5359 - val_accuracy: 0.7500
Epoch 75/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4581 - accuracy: 0.8250 - val_loss: 0.5420 - val_accuracy: 0.7437
Epoch 76/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4563 - accuracy: 0.8281 - val_loss: 0.5400 - val_accuracy: 0.7437
Epoch 77/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4529 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4529 - accuracy: 0.8234 - val_loss: 0.5335 - val_accuracy: 0.7500
Epoch 78/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4558 - accuracy: 0.8250 - val_loss: 0.5324 - val_accuracy: 0.7563
Epoch 79/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4543 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4543 - accuracy: 0.8203 - val_loss: 0.5369 - val_accuracy: 0.7563
Epoch 80/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4520 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4520 - accuracy: 0.8281 - val_loss: 0.5363 - val_accuracy: 0.7563
Epoch 81/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4526 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4526 - accuracy: 0.8313 - val_loss: 0.5297 - val_accuracy: 0.7563
Epoch 82/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4515 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4515 - accuracy: 0.8203 - val_loss: 0.5292 - val_accuracy: 0.7625
Epoch 83/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4521 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4521 - accuracy: 0.8234 - val_loss: 0.5392 - val_accuracy: 0.7437
Epoch 84/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4543 - accuracy: 0.8203 - val_loss: 0.5398 - val_accuracy: 0.7437
Epoch 85/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4515 - accuracy: 0.8219 - val_loss: 0.5281 - val_accuracy: 0.7563
Epoch 86/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4524 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4524 - accuracy: 0.8203 - val_loss: 0.5323 - val_accuracy: 0.7563
Epoch 87/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4486 - accuracy: 0.8203 - val_loss: 0.5393 - val_accuracy: 0.7437
Epoch 88/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4551 - accuracy: 0.8266 - val_loss: 0.5379 - val_accuracy: 0.7437
Epoch 89/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4513 - accuracy: 0.8234 - val_loss: 0.5364 - val_accuracy: 0.7563
Epoch 90/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4532 - accuracy: 0.8250

640/640 [==============================] - 1s 2ms/step - loss: 0.4532 - accuracy: 0.8250 - val_loss: 0.5384 - val_accuracy: 0.7437
Epoch 91/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4502 - accuracy: 0.8188 - val_loss: 0.5359 - val_accuracy: 0.7625
Epoch 92/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4491 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4491 - accuracy: 0.8172 - val_loss: 0.5323 - val_accuracy: 0.7500
Epoch 93/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4502 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4502 - accuracy: 0.8266 - val_loss: 0.5399 - val_accuracy: 0.7437
Epoch 94/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4491 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4491 - accuracy: 0.8344 - val_loss: 0.5455 - val_accuracy: 0.7688
Epoch 95/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4518 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4518 - accuracy: 0.8203 - val_loss: 0.5369 - val_accuracy: 0.7625
Epoch 96/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4506 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4506 - accuracy: 0.8266 - val_loss: 0.5341 - val_accuracy: 0.7500
Epoch 97/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4521 - accuracy: 0.8297

640/640 [==============================] - 1s 2ms/step - loss: 0.4521 - accuracy: 0.8297 - val_loss: 0.5393 - val_accuracy: 0.7500
Epoch 98/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4514 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4514 - accuracy: 0.8219 - val_loss: 0.5356 - val_accuracy: 0.7563
Epoch 99/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4523 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4523 - accuracy: 0.8281 - val_loss: 0.5317 - val_accuracy: 0.7500
Epoch 100/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4509 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4509 - accuracy: 0.8188 - val_loss: 0.5376 - val_accuracy: 0.7500

  
pred.prob <- predict(model.8n, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.8n <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

# New Hidden Layer [number of neurons = 8] & Batch Size = 1
model.9n <- create_model.n(1, 9)

Epoch 1/100

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640/640 [==============================] - 1s 2ms/step - loss: 1.2410 - accuracy: 0.4031

640/640 [==============================] - 2s 3ms/step - loss: 1.2410 - accuracy: 0.4031 - val_loss: 1.0079 - val_accuracy: 0.5688
Epoch 2/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.9490 - accuracy: 0.5969

640/640 [==============================] - 1s 2ms/step - loss: 0.9490 - accuracy: 0.5969 - val_loss: 0.8050 - val_accuracy: 0.6625
Epoch 3/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.7688 - accuracy: 0.6687

640/640 [==============================] - 1s 2ms/step - loss: 0.7688 - accuracy: 0.6687 - val_loss: 0.7208 - val_accuracy: 0.6500
Epoch 4/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6941 - accuracy: 0.7016

640/640 [==============================] - 1s 2ms/step - loss: 0.6941 - accuracy: 0.7016 - val_loss: 0.6728 - val_accuracy: 0.6812
Epoch 5/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6534 - accuracy: 0.7469

640/640 [==============================] - 1s 2ms/step - loss: 0.6534 - accuracy: 0.7469 - val_loss: 0.6494 - val_accuracy: 0.7000
Epoch 6/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6339 - accuracy: 0.7453

640/640 [==============================] - 1s 2ms/step - loss: 0.6339 - accuracy: 0.7453 - val_loss: 0.6295 - val_accuracy: 0.7375
Epoch 7/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.6126 - accuracy: 0.7594

640/640 [==============================] - 1s 2ms/step - loss: 0.6126 - accuracy: 0.7594 - val_loss: 0.6203 - val_accuracy: 0.7375
Epoch 8/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.6010 - accuracy: 0.7656

640/640 [==============================] - 1s 2ms/step - loss: 0.6010 - accuracy: 0.7656 - val_loss: 0.6111 - val_accuracy: 0.7188
Epoch 9/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5927 - accuracy: 0.7672

640/640 [==============================] - 1s 2ms/step - loss: 0.5927 - accuracy: 0.7672 - val_loss: 0.6006 - val_accuracy: 0.7375
Epoch 10/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5843 - accuracy: 0.7719

640/640 [==============================] - 1s 2ms/step - loss: 0.5843 - accuracy: 0.7719 - val_loss: 0.5951 - val_accuracy: 0.7563
Epoch 11/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5757 - accuracy: 0.7812

640/640 [==============================] - 1s 2ms/step - loss: 0.5757 - accuracy: 0.7812 - val_loss: 0.5935 - val_accuracy: 0.7625
Epoch 12/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5696 - accuracy: 0.7703

640/640 [==============================] - 1s 2ms/step - loss: 0.5696 - accuracy: 0.7703 - val_loss: 0.5911 - val_accuracy: 0.7250
Epoch 13/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5661 - accuracy: 0.7766

640/640 [==============================] - 1s 2ms/step - loss: 0.5661 - accuracy: 0.7766 - val_loss: 0.5786 - val_accuracy: 0.7625
Epoch 14/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5555 - accuracy: 0.7750

640/640 [==============================] - 1s 2ms/step - loss: 0.5555 - accuracy: 0.7750 - val_loss: 0.5722 - val_accuracy: 0.7625
Epoch 15/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5518 - accuracy: 0.7875

640/640 [==============================] - 2s 3ms/step - loss: 0.5518 - accuracy: 0.7875 - val_loss: 0.5745 - val_accuracy: 0.7437
Epoch 16/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5474 - accuracy: 0.7828

640/640 [==============================] - 1s 2ms/step - loss: 0.5474 - accuracy: 0.7828 - val_loss: 0.5736 - val_accuracy: 0.7563
Epoch 17/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5412 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.5412 - accuracy: 0.7922 - val_loss: 0.5704 - val_accuracy: 0.7625
Epoch 18/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5342 - accuracy: 0.7797

640/640 [==============================] - 1s 2ms/step - loss: 0.5342 - accuracy: 0.7797 - val_loss: 0.5649 - val_accuracy: 0.7625
Epoch 19/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5300 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.5300 - accuracy: 0.7937 - val_loss: 0.5612 - val_accuracy: 0.7500
Epoch 20/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5253 - accuracy: 0.7891

640/640 [==============================] - 1s 2ms/step - loss: 0.5253 - accuracy: 0.7891 - val_loss: 0.5539 - val_accuracy: 0.7625
Epoch 21/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5197 - accuracy: 0.7906

640/640 [==============================] - 1s 2ms/step - loss: 0.5197 - accuracy: 0.7906 - val_loss: 0.5589 - val_accuracy: 0.7500
Epoch 22/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5184 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5184 - accuracy: 0.7953 - val_loss: 0.5424 - val_accuracy: 0.7563
Epoch 23/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5112 - accuracy: 0.7953

640/640 [==============================] - 1s 2ms/step - loss: 0.5112 - accuracy: 0.7953 - val_loss: 0.5450 - val_accuracy: 0.7500
Epoch 24/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5096 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.5096 - accuracy: 0.7937 - val_loss: 0.5338 - val_accuracy: 0.7563
Epoch 25/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.5030 - accuracy: 0.7937

640/640 [==============================] - 1s 2ms/step - loss: 0.5030 - accuracy: 0.7937 - val_loss: 0.5325 - val_accuracy: 0.7625
Epoch 26/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.5025 - accuracy: 0.7922

640/640 [==============================] - 1s 2ms/step - loss: 0.5025 - accuracy: 0.7922 - val_loss: 0.5327 - val_accuracy: 0.7437
Epoch 27/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4969 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4969 - accuracy: 0.8078 - val_loss: 0.5354 - val_accuracy: 0.7688
Epoch 28/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4974 - accuracy: 0.8078

640/640 [==============================] - 1s 2ms/step - loss: 0.4974 - accuracy: 0.8078 - val_loss: 0.5387 - val_accuracy: 0.7563
Epoch 29/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4919 - accuracy: 0.7953 - val_loss: 0.5342 - val_accuracy: 0.7563
Epoch 30/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4890 - accuracy: 0.8000 - val_loss: 0.5370 - val_accuracy: 0.7625
Epoch 31/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4904 - accuracy: 0.8094 - val_loss: 0.5375 - val_accuracy: 0.7625
Epoch 32/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4870 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4870 - accuracy: 0.8062 - val_loss: 0.5245 - val_accuracy: 0.7563
Epoch 33/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4850 - accuracy: 0.8062 - val_loss: 0.5208 - val_accuracy: 0.7688
Epoch 34/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4819 - accuracy: 0.8094 - val_loss: 0.5241 - val_accuracy: 0.7688
Epoch 35/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4803 - accuracy: 0.8062 - val_loss: 0.5278 - val_accuracy: 0.7688
Epoch 36/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4814 - accuracy: 0.8062

640/640 [==============================] - 1s 2ms/step - loss: 0.4814 - accuracy: 0.8062 - val_loss: 0.5288 - val_accuracy: 0.7625
Epoch 37/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4797 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4797 - accuracy: 0.8188 - val_loss: 0.5288 - val_accuracy: 0.7688
Epoch 38/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4743 - accuracy: 0.8078 - val_loss: 0.5301 - val_accuracy: 0.7500
Epoch 39/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4782 - accuracy: 0.8047 - val_loss: 0.5252 - val_accuracy: 0.7625
Epoch 40/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4766 - accuracy: 0.8172 - val_loss: 0.5288 - val_accuracy: 0.7688
Epoch 41/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4753 - accuracy: 0.8172 - val_loss: 0.5320 - val_accuracy: 0.7500
Epoch 42/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4760 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4760 - accuracy: 0.8125 - val_loss: 0.5235 - val_accuracy: 0.7688
Epoch 43/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4754 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4754 - accuracy: 0.8094 - val_loss: 0.5244 - val_accuracy: 0.7688
Epoch 44/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4754 - accuracy: 0.8109

640/640 [==============================] - 1s 2ms/step - loss: 0.4754 - accuracy: 0.8109 - val_loss: 0.5250 - val_accuracy: 0.7688
Epoch 45/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4686 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4686 - accuracy: 0.8234 - val_loss: 0.5414 - val_accuracy: 0.7563
Epoch 46/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4727 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4727 - accuracy: 0.8188 - val_loss: 0.5340 - val_accuracy: 0.7688
Epoch 47/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4741 - accuracy: 0.8125

640/640 [==============================] - 1s 2ms/step - loss: 0.4741 - accuracy: 0.8125 - val_loss: 0.5284 - val_accuracy: 0.7625
Epoch 48/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4708 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4708 - accuracy: 0.8172 - val_loss: 0.5349 - val_accuracy: 0.7750
Epoch 49/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4725 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4725 - accuracy: 0.8188 - val_loss: 0.5319 - val_accuracy: 0.7563
Epoch 50/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4691 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4691 - accuracy: 0.8234 - val_loss: 0.5356 - val_accuracy: 0.7750
Epoch 51/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4707 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4707 - accuracy: 0.8188 - val_loss: 0.5232 - val_accuracy: 0.7750
Epoch 52/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4703 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4703 - accuracy: 0.8203 - val_loss: 0.5365 - val_accuracy: 0.7750
Epoch 53/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4711 - accuracy: 0.8109 - val_loss: 0.5317 - val_accuracy: 0.7750
Epoch 54/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4691 - accuracy: 0.8156

640/640 [==============================] - 1s 2ms/step - loss: 0.4691 - accuracy: 0.8156 - val_loss: 0.5343 - val_accuracy: 0.7688
Epoch 55/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4675 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4675 - accuracy: 0.8203 - val_loss: 0.5363 - val_accuracy: 0.7750
Epoch 56/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4655 - accuracy: 0.8094

640/640 [==============================] - 1s 2ms/step - loss: 0.4655 - accuracy: 0.8094 - val_loss: 0.5436 - val_accuracy: 0.7750
Epoch 57/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4665 - accuracy: 0.8250

640/640 [==============================] - 1s 2ms/step - loss: 0.4665 - accuracy: 0.8250 - val_loss: 0.5460 - val_accuracy: 0.7688
Epoch 58/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4655 - accuracy: 0.8109 - val_loss: 0.5405 - val_accuracy: 0.7812
Epoch 59/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4673 - accuracy: 0.8203 - val_loss: 0.5358 - val_accuracy: 0.7688
Epoch 60/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4668 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4668 - accuracy: 0.8172 - val_loss: 0.5275 - val_accuracy: 0.7812
Epoch 61/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4655 - accuracy: 0.8125 - val_loss: 0.5463 - val_accuracy: 0.7688
Epoch 62/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4647 - accuracy: 0.8156 - val_loss: 0.5359 - val_accuracy: 0.7625
Epoch 63/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4642 - accuracy: 0.8172 - val_loss: 0.5310 - val_accuracy: 0.7688
Epoch 64/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4622 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4622 - accuracy: 0.8313 - val_loss: 0.5467 - val_accuracy: 0.7750
Epoch 65/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4641 - accuracy: 0.8141

640/640 [==============================] - 1s 2ms/step - loss: 0.4641 - accuracy: 0.8141 - val_loss: 0.5371 - val_accuracy: 0.7688
Epoch 66/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4638 - accuracy: 0.8141 - val_loss: 0.5390 - val_accuracy: 0.7688
Epoch 67/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4644 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4644 - accuracy: 0.8203 - val_loss: 0.5322 - val_accuracy: 0.7812
Epoch 68/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4604 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4604 - accuracy: 0.8219 - val_loss: 0.5367 - val_accuracy: 0.7625
Epoch 69/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4638 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4638 - accuracy: 0.8172 - val_loss: 0.5405 - val_accuracy: 0.7750
Epoch 70/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4616 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4616 - accuracy: 0.8234 - val_loss: 0.5288 - val_accuracy: 0.7812
Epoch 71/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4625 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4625 - accuracy: 0.8172 - val_loss: 0.5305 - val_accuracy: 0.7812
Epoch 72/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4619 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4619 - accuracy: 0.8203 - val_loss: 0.5375 - val_accuracy: 0.7812
Epoch 73/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4609 - accuracy: 0.8188

640/640 [==============================] - 1s 2ms/step - loss: 0.4609 - accuracy: 0.8188 - val_loss: 0.5412 - val_accuracy: 0.7812
Epoch 74/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4605 - accuracy: 0.8219 - val_loss: 0.5457 - val_accuracy: 0.7750
Epoch 75/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4617 - accuracy: 0.8172

640/640 [==============================] - 1s 2ms/step - loss: 0.4617 - accuracy: 0.8172 - val_loss: 0.5403 - val_accuracy: 0.7688
Epoch 76/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4611 - accuracy: 0.8219

640/640 [==============================] - 1s 2ms/step - loss: 0.4611 - accuracy: 0.8219 - val_loss: 0.5494 - val_accuracy: 0.7812
Epoch 77/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4575 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4575 - accuracy: 0.8234 - val_loss: 0.5472 - val_accuracy: 0.7625
Epoch 78/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4581 - accuracy: 0.8250

640/640 [==============================] - 1s 2ms/step - loss: 0.4581 - accuracy: 0.8250 - val_loss: 0.5455 - val_accuracy: 0.7688
Epoch 79/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4582 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4582 - accuracy: 0.8203 - val_loss: 0.5339 - val_accuracy: 0.7688
Epoch 80/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4610 - accuracy: 0.8281

640/640 [==============================] - 1s 2ms/step - loss: 0.4610 - accuracy: 0.8281 - val_loss: 0.5335 - val_accuracy: 0.7750
Epoch 81/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4569 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4569 - accuracy: 0.8313 - val_loss: 0.5412 - val_accuracy: 0.7688
Epoch 82/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4577 - accuracy: 0.8313 - val_loss: 0.5323 - val_accuracy: 0.7812
Epoch 83/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4594 - accuracy: 0.8234

640/640 [==============================] - 1s 2ms/step - loss: 0.4594 - accuracy: 0.8234 - val_loss: 0.5431 - val_accuracy: 0.7688
Epoch 84/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4562 - accuracy: 0.8203

640/640 [==============================] - 1s 2ms/step - loss: 0.4562 - accuracy: 0.8203 - val_loss: 0.5486 - val_accuracy: 0.7750
Epoch 85/100

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640/640 [==============================] - 1s 1ms/step - loss: 0.4573 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4573 - accuracy: 0.8266 - val_loss: 0.5285 - val_accuracy: 0.7812
Epoch 86/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4553 - accuracy: 0.8266 - val_loss: 0.5355 - val_accuracy: 0.7812
Epoch 87/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4563 - accuracy: 0.8219 - val_loss: 0.5431 - val_accuracy: 0.7812
Epoch 88/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4557 - accuracy: 0.8266

640/640 [==============================] - 1s 2ms/step - loss: 0.4557 - accuracy: 0.8266 - val_loss: 0.5345 - val_accuracy: 0.7688
Epoch 89/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4564 - accuracy: 0.8297

640/640 [==============================] - 2s 3ms/step - loss: 0.4564 - accuracy: 0.8297 - val_loss: 0.5420 - val_accuracy: 0.7750
Epoch 90/100

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640/640 [==============================] - 2s 3ms/step - loss: 0.4545 - accuracy: 0.8266 - val_loss: 0.5559 - val_accuracy: 0.7688
Epoch 91/100

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640/640 [==============================] - 3s 4ms/step - loss: 0.4557 - accuracy: 0.8219 - val_loss: 0.5433 - val_accuracy: 0.7625
Epoch 92/100

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640/640 [==============================] - 2s 3ms/step - loss: 0.4530 - accuracy: 0.8297

640/640 [==============================] - 3s 4ms/step - loss: 0.4530 - accuracy: 0.8297 - val_loss: 0.5486 - val_accuracy: 0.7750
Epoch 93/100

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640/640 [==============================] - 3s 5ms/step - loss: 0.4565 - accuracy: 0.8266 - val_loss: 0.5352 - val_accuracy: 0.7812
Epoch 94/100

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640/640 [==============================] - 2s 4ms/step - loss: 0.4521 - accuracy: 0.8359 - val_loss: 0.5457 - val_accuracy: 0.7812
Epoch 95/100

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640/640 [==============================] - 1s 2ms/step - loss: 0.4548 - accuracy: 0.8281 - val_loss: 0.5494 - val_accuracy: 0.7500
Epoch 96/100

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640/640 [==============================] - 2s 2ms/step - loss: 0.4541 - accuracy: 0.8281 - val_loss: 0.5392 - val_accuracy: 0.7750
Epoch 97/100

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640/640 [==============================] - 2s 2ms/step - loss: 0.4520 - accuracy: 0.8328 - val_loss: 0.5379 - val_accuracy: 0.7812
Epoch 98/100

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613/640 [===========================>..] - ETA: 0s - loss: 0.4486 - accuracy: 0.8336
640/640 [==============================] - 1s 2ms/step - loss: 0.4507 - accuracy: 0.8313

640/640 [==============================] - 1s 2ms/step - loss: 0.4507 - accuracy: 0.8313 - val_loss: 0.5417 - val_accuracy: 0.7875
Epoch 99/100

  1/640 [..............................] - ETA: 0s - loss: 0.2482 - accuracy: 1.0000
 45/640 [=>............................] - ETA: 0s - loss: 0.5144 - accuracy: 0.8667
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640/640 [==============================] - 1s 1ms/step - loss: 0.4506 - accuracy: 0.8344

640/640 [==============================] - 1s 2ms/step - loss: 0.4506 - accuracy: 0.8344 - val_loss: 0.5445 - val_accuracy: 0.7812
Epoch 100/100

  1/640 [..............................] - ETA: 3s - loss: 3.3576 - accuracy: 0.0000e+00
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608/640 [===========================>..] - ETA: 0s - loss: 0.4465 - accuracy: 0.8306
640/640 [==============================] - 1s 2ms/step - loss: 0.4522 - accuracy: 0.8313

640/640 [==============================] - 2s 3ms/step - loss: 0.4522 - accuracy: 0.8313 - val_loss: 0.5499 - val_accuracy: 0.7563

  
pred.prob <- predict(model.9n, as.matrix(x.test.df))
pred.class <- apply(pred.prob, 1, function(x) which.max(x)-1)
confusion.matrix.9n <- confusionMatrix(as.factor(pred.class), as.factor(y.test.df))

---

Part 2.1(d)(i) & Part 2.1(d)(ii)

---

  print(paste('Second Hidden Layer of Neurons : ', 6))

[1] "Second Hidden Layer of Neurons :  6"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.6n[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.79"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.6n[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.6n[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.6n[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.91 , Specificity =  0.98 , Balanced Accuracy =  0.95"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.6n[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.6n[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.6n[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.81 , Specificity =  0.87 , Balanced Accuracy =  0.84"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.6n[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.6n[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.6n[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.93 , Specificity =  0.92 , Balanced Accuracy =  0.93"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.6n[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.6n[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.6n[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.5 , Specificity =  0.95 , Balanced Accuracy =  0.72"

  print(paste('Second Hidden Layer of Neurons : ', 7))

[1] "Second Hidden Layer of Neurons :  7"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.7n[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.83"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.7n[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.7n[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.7n[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.93 , Specificity =  0.97 , Balanced Accuracy =  0.95"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.7n[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.7n[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.7n[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.75 , Specificity =  0.95 , Balanced Accuracy =  0.85"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.7n[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.7n[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.7n[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.88 , Specificity =  0.92 , Balanced Accuracy =  0.9"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.7n[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.7n[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.7n[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.75 , Specificity =  0.93 , Balanced Accuracy =  0.84"

  print(paste('Second Hidden Layer of Neurons : ', 8))

[1] "Second Hidden Layer of Neurons :  8"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.8n[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.8"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.8n[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.8n[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.8n[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.93 , Specificity =  0.97 , Balanced Accuracy =  0.95"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.8n[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.8n[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.8n[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.7 , Specificity =  0.93 , Balanced Accuracy =  0.81"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.8n[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.8n[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.8n[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.93 , Specificity =  0.92 , Balanced Accuracy =  0.93"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.8n[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.8n[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.8n[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.65 , Specificity =  0.92 , Balanced Accuracy =  0.78"

  print(paste('Second Hidden Layer of Neurons : ', 9))

[1] "Second Hidden Layer of Neurons :  9"

  print(paste("Overall Accuracy: ", signif(confusion.matrix.9n[["overall"]][["Accuracy"]], 2)))

[1] "Overall Accuracy:  0.78"

  print(paste('   ', 'Class 0: Sensitivity = ', signif(confusion.matrix.9n[["byClass"]][1,1], 2), ", Specificity = ", signif(confusion.matrix.9n[["byClass"]][1,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.9n[["byClass"]][1,11], 2)))

[1] "    Class 0: Sensitivity =  0.93 , Specificity =  0.97 , Balanced Accuracy =  0.95"

  print(paste('   ', 'Class 1: Sensitivity = ', signif(confusion.matrix.9n[["byClass"]][2,1], 2), ", Specificity = ", signif(confusion.matrix.9n[["byClass"]][2,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.9n[["byClass"]][2,11], 2)))

[1] "    Class 1: Sensitivity =  0.68 , Specificity =  0.92 , Balanced Accuracy =  0.8"

  print(paste('   ', 'Class 2: Sensitivity = ', signif(confusion.matrix.9n[["byClass"]][3,1], 2), ", Specificity = ", signif(confusion.matrix.9n[["byClass"]][3,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.9n[["byClass"]][3,11], 2)))

[1] "    Class 2: Sensitivity =  0.93 , Specificity =  0.91 , Balanced Accuracy =  0.92"

  print(paste('   ', 'Class 3: Sensitivity = ', signif(confusion.matrix.9n[["byClass"]][4,1], 2), ", Specificity = ", signif(confusion.matrix.9n[["byClass"]][4,2], 2), ", Balanced Accuracy = ", signif(confusion.matrix.9n[["byClass"]][4,11], 2)))

[1] "    Class 3: Sensitivity =  0.6 , Specificity =  0.91 , Balanced Accuracy =  0.76"

---

Part(2.1)(d)(ii)(a)

---

I took between 6 and 9 the numbers of neurons that I thought would give me the best chance of improving my accuracy. After iterating through numerous number of neurons, adding another hidden layer doesn’t necessarily mean that your model will improve. In my case, I found the number 7 of the secondary hidden layer to be good as it significantly improved my model’s overall accuracy to 0.83.

---